Overview

Dataset statistics

Number of variables43
Number of observations1311
Missing cells3885
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory482.9 KiB
Average record size in memory377.2 B

Variable types

Categorical24
Numeric19

Alerts

Species has constant value "Arabica"Constant
Owner has a high cardinality: 305 distinct valuesHigh cardinality
Farm.Name has a high cardinality: 557 distinct valuesHigh cardinality
Lot.Number has a high cardinality: 221 distinct valuesHigh cardinality
Mill has a high cardinality: 447 distinct valuesHigh cardinality
ICO.Number has a high cardinality: 841 distinct valuesHigh cardinality
Company has a high cardinality: 270 distinct valuesHigh cardinality
Altitude has a high cardinality: 383 distinct valuesHigh cardinality
Region has a high cardinality: 343 distinct valuesHigh cardinality
Producer has a high cardinality: 675 distinct valuesHigh cardinality
Bag.Weight has a high cardinality: 56 distinct valuesHigh cardinality
Grading.Date has a high cardinality: 558 distinct valuesHigh cardinality
Owner.1 has a high cardinality: 309 distinct valuesHigh cardinality
Expiration has a high cardinality: 557 distinct valuesHigh cardinality
Aroma is highly overall correlated with Flavor and 6 other fieldsHigh correlation
Flavor is highly overall correlated with Aroma and 6 other fieldsHigh correlation
Aftertaste is highly overall correlated with Aroma and 6 other fieldsHigh correlation
Acidity is highly overall correlated with Aroma and 6 other fieldsHigh correlation
Body is highly overall correlated with Aroma and 6 other fieldsHigh correlation
Balance is highly overall correlated with Aroma and 6 other fieldsHigh correlation
Uniformity is highly overall correlated with Clean.CupHigh correlation
Clean.Cup is highly overall correlated with UniformityHigh correlation
Cupper.Points is highly overall correlated with Aroma and 6 other fieldsHigh correlation
Total.Cup.Points is highly overall correlated with Aroma and 6 other fieldsHigh correlation
altitude_low_meters is highly overall correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly overall correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly overall correlated with altitude_low_meters and 1 other fieldsHigh correlation
Country.of.Origin is highly overall correlated with In.Country.Partner and 4 other fieldsHigh correlation
Bag.Weight is highly overall correlated with unit_of_measurementHigh correlation
In.Country.Partner is highly overall correlated with Country.of.Origin and 4 other fieldsHigh correlation
Variety is highly overall correlated with unit_of_measurementHigh correlation
Certification.Body is highly overall correlated with Country.of.Origin and 4 other fieldsHigh correlation
Certification.Address is highly overall correlated with Country.of.Origin and 4 other fieldsHigh correlation
Certification.Contact is highly overall correlated with Country.of.Origin and 4 other fieldsHigh correlation
unit_of_measurement is highly overall correlated with Country.of.Origin and 6 other fieldsHigh correlation
Farm.Name has 356 (27.2%) missing valuesMissing
Lot.Number has 1041 (79.4%) missing valuesMissing
Mill has 310 (23.6%) missing valuesMissing
ICO.Number has 146 (11.1%) missing valuesMissing
Company has 209 (15.9%) missing valuesMissing
Altitude has 223 (17.0%) missing valuesMissing
Region has 57 (4.3%) missing valuesMissing
Producer has 230 (17.5%) missing valuesMissing
Harvest.Year has 47 (3.6%) missing valuesMissing
Variety has 201 (15.3%) missing valuesMissing
Processing.Method has 152 (11.6%) missing valuesMissing
Color has 216 (16.5%) missing valuesMissing
altitude_low_meters has 227 (17.3%) missing valuesMissing
altitude_high_meters has 227 (17.3%) missing valuesMissing
altitude_mean_meters has 227 (17.3%) missing valuesMissing
altitude_low_meters is highly skewed (γ1 = 20.0978744)Skewed
altitude_high_meters is highly skewed (γ1 = 20.08565748)Skewed
altitude_mean_meters is highly skewed (γ1 = 20.09518078)Skewed
Moisture has 252 (19.2%) zerosZeros
Category.One.Defects has 1111 (84.7%) zerosZeros
Quakers has 1216 (92.8%) zerosZeros
Category.Two.Defects has 362 (27.6%) zerosZeros

Reproduction

Analysis started2023-07-24 16:56:01.140433
Analysis finished2023-07-24 16:57:09.070219
Duration1 minute and 7.93 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Species
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
Arabica
1311 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters9177
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArabica
2nd rowArabica
3rd rowArabica
4th rowArabica
5th rowArabica

Common Values

ValueCountFrequency (%)
Arabica 1311
100.0%

Length

2023-07-24T18:57:09.182925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T18:57:09.339715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
arabica 1311
100.0%

Most occurring characters

ValueCountFrequency (%)
a 2622
28.6%
A 1311
14.3%
r 1311
14.3%
b 1311
14.3%
i 1311
14.3%
c 1311
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7866
85.7%
Uppercase Letter 1311
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2622
33.3%
r 1311
16.7%
b 1311
16.7%
i 1311
16.7%
c 1311
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 1311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9177
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2622
28.6%
A 1311
14.3%
r 1311
14.3%
b 1311
14.3%
i 1311
14.3%
c 1311
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2622
28.6%
A 1311
14.3%
r 1311
14.3%
b 1311
14.3%
i 1311
14.3%
c 1311
14.3%

Owner
Categorical

Distinct305
Distinct (%)23.4%
Missing7
Missing (%)0.5%
Memory size20.5 KiB
juan luis alvarado romero
155 
racafe & cia s.c.a
 
60
exportadora de cafe condor s.a
 
54
kona pacific farmers cooperative
 
52
ipanema coffees
 
50
Other values (300)
933 

Length

Max length50
Median length40
Mean length21.347393
Min length3

Characters and Unicode

Total characters27837
Distinct characters51
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)12.8%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowgrounds for health admin
4th rowyidnekachew dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
juan luis alvarado romero 155
 
11.8%
racafe & cia s.c.a 60
 
4.6%
exportadora de cafe condor s.a 54
 
4.1%
kona pacific farmers cooperative 52
 
4.0%
ipanema coffees 50
 
3.8%
cqi taiwan icp cqi台灣合作夥伴 47
 
3.6%
lin, che-hao krude 林哲豪 30
 
2.3%
nucoffee 29
 
2.2%
carcafe ltda ci 27
 
2.1%
the coffee source inc. 23
 
1.8%
Other values (295) 777
59.3%

Length

2023-07-24T18:57:09.512028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis 167
 
3.9%
juan 160
 
3.8%
alvarado 155
 
3.6%
romero 155
 
3.6%
de 114
 
2.7%
s.a 101
 
2.4%
coffee 83
 
1.9%
cafe 72
 
1.7%
exportadora 70
 
1.6%
coffees 67
 
1.6%
Other values (642) 3115
73.1%

Most occurring characters

ValueCountFrequency (%)
a 3397
12.2%
2963
 
10.6%
e 2534
 
9.1%
o 2173
 
7.8%
r 1962
 
7.0%
i 1619
 
5.8%
c 1614
 
5.8%
n 1326
 
4.8%
l 1127
 
4.0%
s 1087
 
3.9%
Other values (41) 8035
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23889
85.8%
Space Separator 2963
 
10.6%
Other Punctuation 512
 
1.8%
Other Letter 404
 
1.5%
Dash Punctuation 43
 
0.2%
Open Punctuation 13
 
< 0.1%
Close Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3397
14.2%
e 2534
10.6%
o 2173
 
9.1%
r 1962
 
8.2%
i 1619
 
6.8%
c 1614
 
6.8%
n 1326
 
5.6%
l 1127
 
4.7%
s 1087
 
4.6%
d 945
 
4.0%
Other values (21) 6105
25.6%
Other Letter
ValueCountFrequency (%)
灣 47
11.6%
伴 47
11.6%
夥 47
11.6%
作 47
11.6%
合 47
11.6%
台 47
11.6%
林 30
7.4%
哲 30
7.4%
豪 30
7.4%
宏 8
 
2.0%
Other values (3) 24
5.9%
Other Punctuation
ValueCountFrequency (%)
. 377
73.6%
, 72
 
14.1%
& 63
 
12.3%
Space Separator
ValueCountFrequency (%)
2963
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23889
85.8%
Common 3544
 
12.7%
Han 404
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3397
14.2%
e 2534
10.6%
o 2173
 
9.1%
r 1962
 
8.2%
i 1619
 
6.8%
c 1614
 
6.8%
n 1326
 
5.6%
l 1127
 
4.7%
s 1087
 
4.6%
d 945
 
4.0%
Other values (21) 6105
25.6%
Han
ValueCountFrequency (%)
灣 47
11.6%
伴 47
11.6%
夥 47
11.6%
作 47
11.6%
合 47
11.6%
台 47
11.6%
林 30
7.4%
哲 30
7.4%
豪 30
7.4%
宏 8
 
2.0%
Other values (3) 24
5.9%
Common
ValueCountFrequency (%)
2963
83.6%
. 377
 
10.6%
, 72
 
2.0%
& 63
 
1.8%
- 43
 
1.2%
( 13
 
0.4%
) 13
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27392
98.4%
CJK 404
 
1.5%
None 41
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3397
12.4%
2963
 
10.8%
e 2534
 
9.3%
o 2173
 
7.9%
r 1962
 
7.2%
i 1619
 
5.9%
c 1614
 
5.9%
n 1326
 
4.8%
l 1127
 
4.1%
s 1087
 
4.0%
Other values (23) 7590
27.7%
CJK
ValueCountFrequency (%)
灣 47
11.6%
伴 47
11.6%
夥 47
11.6%
作 47
11.6%
合 47
11.6%
台 47
11.6%
林 30
7.4%
哲 30
7.4%
豪 30
7.4%
宏 8
 
2.0%
Other values (3) 24
5.9%
None
ValueCountFrequency (%)
ñ 23
56.1%
é 12
29.3%
á 3
 
7.3%
ú 2
 
4.9%
ó 1
 
2.4%

Country.of.Origin
Categorical

Distinct36
Distinct (%)2.7%
Missing1
Missing (%)0.1%
Memory size20.5 KiB
Mexico
236 
Colombia
183 
Guatemala
181 
Brazil
132 
Taiwan
75 
Other values (31)
503 

Length

Max length28
Median length27
Mean length8.8931298
Min length4

Characters and Unicode

Total characters11650
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.6%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowGuatemala
4th rowEthiopia
5th rowEthiopia

Common Values

ValueCountFrequency (%)
Mexico 236
18.0%
Colombia 183
14.0%
Guatemala 181
13.8%
Brazil 132
10.1%
Taiwan 75
 
5.7%
United States (Hawaii) 73
 
5.6%
Honduras 53
 
4.0%
Costa Rica 51
 
3.9%
Ethiopia 44
 
3.4%
Tanzania, United Republic Of 40
 
3.1%
Other values (26) 242
18.5%

Length

2023-07-24T18:57:09.699529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico 236
14.1%
colombia 183
 
11.0%
guatemala 181
 
10.8%
brazil 132
 
7.9%
united 125
 
7.5%
states 85
 
5.1%
taiwan 75
 
4.5%
hawaii 73
 
4.4%
honduras 53
 
3.2%
costa 51
 
3.1%
Other values (35) 477
28.5%

Most occurring characters

ValueCountFrequency (%)
a 1933
16.6%
i 1267
 
10.9%
o 805
 
6.9%
e 742
 
6.4%
l 626
 
5.4%
t 590
 
5.1%
n 502
 
4.3%
m 384
 
3.3%
361
 
3.1%
c 358
 
3.1%
Other values (37) 4082
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9423
80.9%
Uppercase Letter 1671
 
14.3%
Space Separator 361
 
3.1%
Open Punctuation 77
 
0.7%
Close Punctuation 77
 
0.7%
Other Punctuation 41
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1933
20.5%
i 1267
13.4%
o 805
8.5%
e 742
 
7.9%
l 626
 
6.6%
t 590
 
6.3%
n 502
 
5.3%
m 384
 
4.1%
c 358
 
3.8%
u 323
 
3.4%
Other values (13) 1893
20.1%
Uppercase Letter
ValueCountFrequency (%)
M 256
15.3%
C 251
15.0%
G 182
10.9%
U 151
9.0%
T 147
8.8%
B 134
8.0%
H 132
7.9%
S 106
6.3%
R 96
 
5.7%
E 66
 
3.9%
Other values (9) 150
9.0%
Other Punctuation
ValueCountFrequency (%)
, 40
97.6%
? 1
 
2.4%
Space Separator
ValueCountFrequency (%)
361
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11094
95.2%
Common 556
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1933
17.4%
i 1267
 
11.4%
o 805
 
7.3%
e 742
 
6.7%
l 626
 
5.6%
t 590
 
5.3%
n 502
 
4.5%
m 384
 
3.5%
c 358
 
3.2%
u 323
 
2.9%
Other values (32) 3564
32.1%
Common
ValueCountFrequency (%)
361
64.9%
( 77
 
13.8%
) 77
 
13.8%
, 40
 
7.2%
? 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1933
16.6%
i 1267
 
10.9%
o 805
 
6.9%
e 742
 
6.4%
l 626
 
5.4%
t 590
 
5.1%
n 502
 
4.3%
m 384
 
3.3%
361
 
3.1%
c 358
 
3.1%
Other values (37) 4082
35.0%

Farm.Name
Categorical

HIGH CARDINALITY  MISSING 

Distinct557
Distinct (%)58.3%
Missing356
Missing (%)27.2%
Memory size20.5 KiB
various
 
47
rio verde
 
23
several
 
20
finca medina
 
15
doi tung development project
 
13
Other values (552)
837 

Length

Max length73
Median length41
Mean length15.24712
Min length1

Characters and Unicode

Total characters14561
Distinct characters211
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique429 ?
Unique (%)44.9%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowsan marcos barrancas "san cristobal cuch
4th rowyidnekachew dabessa coffee plantation
5th rowmetad plc

Common Values

ValueCountFrequency (%)
various 47
 
3.6%
rio verde 23
 
1.8%
several 20
 
1.5%
finca medina 15
 
1.1%
doi tung development project 13
 
1.0%
fazenda capoeirnha 13
 
1.0%
los hicaques 11
 
0.8%
conquista / morito 11
 
0.8%
capoeirinha 10
 
0.8%
el papaturro 9
 
0.7%
Other values (547) 783
59.7%
(Missing) 356
27.2%

Length

2023-07-24T18:57:09.918277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
el 96
 
4.1%
la 87
 
3.7%
finca 79
 
3.4%
coffee 71
 
3.0%
various 47
 
2.0%
santa 40
 
1.7%
40
 
1.7%
estate 38
 
1.6%
fazenda 38
 
1.6%
los 36
 
1.5%
Other values (841) 1762
75.5%

Most occurring characters

ValueCountFrequency (%)
a 1947
13.4%
1403
 
9.6%
e 1291
 
8.9%
o 972
 
6.7%
n 863
 
5.9%
r 823
 
5.7%
i 812
 
5.6%
l 742
 
5.1%
s 653
 
4.5%
c 651
 
4.5%
Other values (201) 4404
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12457
85.6%
Space Separator 1403
 
9.6%
Other Letter 404
 
2.8%
Decimal Number 122
 
0.8%
Other Punctuation 115
 
0.8%
Dash Punctuation 30
 
0.2%
Math Symbol 12
 
0.1%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
咖 46
 
11.4%
啡 45
 
11.1%
園 28
 
6.9%
莊 21
 
5.2%
山 15
 
3.7%
村 6
 
1.5%
台 6
 
1.5%
坊 6
 
1.5%
高 5
 
1.2%
國 5
 
1.2%
Other values (139) 221
54.7%
Lowercase Letter
ValueCountFrequency (%)
a 1947
15.6%
e 1291
10.4%
o 972
 
7.8%
n 863
 
6.9%
r 823
 
6.6%
i 812
 
6.5%
l 742
 
6.0%
s 653
 
5.2%
c 651
 
5.2%
t 572
 
4.6%
Other values (24) 3131
25.1%
Decimal Number
ValueCountFrequency (%)
1 44
36.1%
0 26
21.3%
2 19
15.6%
5 8
 
6.6%
3 6
 
4.9%
4 5
 
4.1%
8 5
 
4.1%
9 5
 
4.1%
7 3
 
2.5%
6 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 43
37.4%
. 34
29.6%
/ 22
19.1%
' 6
 
5.2%
@ 4
 
3.5%
: 2
 
1.7%
& 1
 
0.9%
" 1
 
0.9%
; 1
 
0.9%
# 1
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 29
96.7%
– 1
 
3.3%
Space Separator
ValueCountFrequency (%)
1403
100.0%
Math Symbol
ValueCountFrequency (%)
| 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12457
85.6%
Common 1700
 
11.7%
Han 404
 
2.8%

Most frequent character per script

Han
ValueCountFrequency (%)
咖 46
 
11.4%
啡 45
 
11.1%
園 28
 
6.9%
莊 21
 
5.2%
山 15
 
3.7%
村 6
 
1.5%
台 6
 
1.5%
坊 6
 
1.5%
高 5
 
1.2%
國 5
 
1.2%
Other values (139) 221
54.7%
Latin
ValueCountFrequency (%)
a 1947
15.6%
e 1291
10.4%
o 972
 
7.8%
n 863
 
6.9%
r 823
 
6.6%
i 812
 
6.5%
l 742
 
6.0%
s 653
 
5.2%
c 651
 
5.2%
t 572
 
4.6%
Other values (24) 3131
25.1%
Common
ValueCountFrequency (%)
1403
82.5%
1 44
 
2.6%
, 43
 
2.5%
. 34
 
2.0%
- 29
 
1.7%
0 26
 
1.5%
/ 22
 
1.3%
2 19
 
1.1%
| 12
 
0.7%
5 8
 
0.5%
Other values (18) 60
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14098
96.8%
CJK 404
 
2.8%
None 56
 
0.4%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1947
13.8%
1403
 
10.0%
e 1291
 
9.2%
o 972
 
6.9%
n 863
 
6.1%
r 823
 
5.8%
i 812
 
5.8%
l 742
 
5.3%
s 653
 
4.6%
c 651
 
4.6%
Other values (42) 3941
28.0%
CJK
ValueCountFrequency (%)
咖 46
 
11.4%
啡 45
 
11.1%
園 28
 
6.9%
莊 21
 
5.2%
山 15
 
3.7%
村 6
 
1.5%
台 6
 
1.5%
坊 6
 
1.5%
高 5
 
1.2%
國 5
 
1.2%
Other values (139) 221
54.7%
None
ValueCountFrequency (%)
ñ 14
25.0%
é 11
19.6%
ã 10
17.9%
í 8
14.3%
ó 5
 
8.9%
ú 4
 
7.1%
á 3
 
5.4%
ê 1
 
1.8%
Punctuation
ValueCountFrequency (%)
’ 2
66.7%
– 1
33.3%

Lot.Number
Categorical

HIGH CARDINALITY  MISSING 

Distinct221
Distinct (%)81.9%
Missing1041
Missing (%)79.4%
Memory size20.5 KiB
1
 
18
020/17
 
6
019/17
 
5
102
 
3
103
 
3
Other values (216)
235 

Length

Max length71
Median length46
Mean length10.011111
Min length1

Characters and Unicode

Total characters2703
Distinct characters76
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)73.7%

Sample

1st rowYNC-06114
2nd row102
3rd rowTsoustructive 2015 Sumatra Typica
4th row11/23/0252
5th rowBaby Geisha Washed

Common Values

ValueCountFrequency (%)
1 18
 
1.4%
020/17 6
 
0.5%
019/17 5
 
0.4%
102 3
 
0.2%
103 3
 
0.2%
2 3
 
0.2%
2016 Tainan Coffee Cupping Event Micro Lot 臺南市咖啡評鑑批次 3
 
0.2%
017-053-0046 2
 
0.2%
11/23/0634 2
 
0.2%
43102245/P4615 2
 
0.2%
Other values (211) 223
 
17.0%
(Missing) 1041
79.4%

Length

2023-07-24T18:57:10.137027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 18
 
4.7%
11
 
2.9%
coffee 10
 
2.6%
tainan 6
 
1.6%
lot 6
 
1.6%
020/17 6
 
1.6%
019/17 5
 
1.3%
event 5
 
1.3%
2017 5
 
1.3%
evaluation 5
 
1.3%
Other values (250) 305
79.8%

Most occurring characters

ValueCountFrequency (%)
1 392
14.5%
0 270
 
10.0%
3 183
 
6.8%
2 171
 
6.3%
/ 151
 
5.6%
- 131
 
4.8%
7 126
 
4.7%
120
 
4.4%
6 117
 
4.3%
5 108
 
4.0%
Other values (66) 934
34.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1548
57.3%
Lowercase Letter 381
 
14.1%
Uppercase Letter 290
 
10.7%
Other Punctuation 157
 
5.8%
Dash Punctuation 131
 
4.8%
Space Separator 120
 
4.4%
Other Letter 72
 
2.7%
Format 4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 47
16.2%
N 33
11.4%
T 24
 
8.3%
E 23
 
7.9%
A 22
 
7.6%
L 18
 
6.2%
Y 12
 
4.1%
M 12
 
4.1%
P 10
 
3.4%
K 10
 
3.4%
Other values (14) 79
27.2%
Lowercase Letter
ValueCountFrequency (%)
a 52
13.6%
e 38
10.0%
o 37
9.7%
t 32
 
8.4%
i 32
 
8.4%
n 29
 
7.6%
r 24
 
6.3%
s 19
 
5.0%
f 19
 
5.0%
u 18
 
4.7%
Other values (12) 81
21.3%
Other Letter
ValueCountFrequency (%)
咖 8
11.1%
鑑 8
11.1%
評 8
11.1%
啡 8
11.1%
南 8
11.1%
批 6
8.3%
次 6
8.3%
市 6
8.3%
品 3
 
4.2%
精 3
 
4.2%
Other values (3) 8
11.1%
Decimal Number
ValueCountFrequency (%)
1 392
25.3%
0 270
17.4%
3 183
11.8%
2 171
11.0%
7 126
 
8.1%
6 117
 
7.6%
5 108
 
7.0%
4 64
 
4.1%
9 61
 
3.9%
8 56
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 151
96.2%
. 4
 
2.5%
: 1
 
0.6%
# 1
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 131
100.0%
Space Separator
ValueCountFrequency (%)
120
100.0%
Format
ValueCountFrequency (%)
⁠ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1960
72.5%
Latin 671
 
24.8%
Han 72
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 52
 
7.7%
C 47
 
7.0%
e 38
 
5.7%
o 37
 
5.5%
N 33
 
4.9%
t 32
 
4.8%
i 32
 
4.8%
n 29
 
4.3%
r 24
 
3.6%
T 24
 
3.6%
Other values (36) 323
48.1%
Common
ValueCountFrequency (%)
1 392
20.0%
0 270
13.8%
3 183
9.3%
2 171
8.7%
/ 151
 
7.7%
- 131
 
6.7%
7 126
 
6.4%
120
 
6.1%
6 117
 
6.0%
5 108
 
5.5%
Other values (7) 191
9.7%
Han
ValueCountFrequency (%)
咖 8
11.1%
鑑 8
11.1%
評 8
11.1%
啡 8
11.1%
南 8
11.1%
批 6
8.3%
次 6
8.3%
市 6
8.3%
品 3
 
4.2%
精 3
 
4.2%
Other values (3) 8
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2627
97.2%
CJK 72
 
2.7%
Punctuation 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 392
14.9%
0 270
 
10.3%
3 183
 
7.0%
2 171
 
6.5%
/ 151
 
5.7%
- 131
 
5.0%
7 126
 
4.8%
120
 
4.6%
6 117
 
4.5%
5 108
 
4.1%
Other values (52) 858
32.7%
CJK
ValueCountFrequency (%)
咖 8
11.1%
鑑 8
11.1%
評 8
11.1%
啡 8
11.1%
南 8
11.1%
批 6
8.3%
次 6
8.3%
市 6
8.3%
品 3
 
4.2%
精 3
 
4.2%
Other values (3) 8
11.1%
Punctuation
ValueCountFrequency (%)
⁠ 4
100.0%

Mill
Categorical

HIGH CARDINALITY  MISSING 

Distinct447
Distinct (%)44.7%
Missing310
Missing (%)23.6%
Memory size20.5 KiB
beneficio ixchel
90 
dry mill
 
39
trilladora boananza
 
38
ipanema coffees
 
16
neiva
 
15
Other values (442)
803 

Length

Max length77
Median length52
Mean length19.131868
Min length1

Characters and Unicode

Total characters19151
Distinct characters190
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300 ?
Unique (%)30.0%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowwolensu
4th rowmetad plc
5th rowhvc

Common Values

ValueCountFrequency (%)
beneficio ixchel 90
 
6.9%
dry mill 39
 
3.0%
trilladora boananza 38
 
2.9%
ipanema coffees 16
 
1.2%
neiva 15
 
1.1%
bachue 14
 
1.1%
cadexsa 12
 
0.9%
cigrah s.a de c.v. 12
 
0.9%
trilladora bonanza - armenia quindioa 12
 
0.9%
beneficio siembras vision (154) 12
 
0.9%
Other values (437) 741
56.5%
(Missing) 310
23.6%

Length

2023-07-24T18:57:10.387025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beneficio 181
 
6.3%
de 105
 
3.6%
ixchel 93
 
3.2%
coffee 88
 
3.0%
trilladora 68
 
2.4%
mill 48
 
1.7%
dry 46
 
1.6%
la 39
 
1.3%
el 38
 
1.3%
boananza 38
 
1.3%
Other values (781) 2146
74.3%

Most occurring characters

ValueCountFrequency (%)
a 2230
 
11.6%
1913
 
10.0%
e 1775
 
9.3%
i 1434
 
7.5%
o 1292
 
6.7%
c 1156
 
6.0%
n 1101
 
5.7%
l 940
 
4.9%
r 889
 
4.6%
s 673
 
3.5%
Other values (180) 5748
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16390
85.6%
Space Separator 1913
 
10.0%
Other Letter 379
 
2.0%
Other Punctuation 306
 
1.6%
Decimal Number 75
 
0.4%
Dash Punctuation 34
 
0.2%
Close Punctuation 26
 
0.1%
Open Punctuation 26
 
0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
咖 47
 
12.4%
啡 45
 
11.9%
園 22
 
5.8%
莊 15
 
4.0%
山 14
 
3.7%
台 7
 
1.8%
坊 6
 
1.6%
里 5
 
1.3%
國 5
 
1.3%
高 5
 
1.3%
Other values (129) 208
54.9%
Lowercase Letter
ValueCountFrequency (%)
a 2230
13.6%
e 1775
10.8%
i 1434
 
8.7%
o 1292
 
7.9%
c 1156
 
7.1%
n 1101
 
6.7%
l 940
 
5.7%
r 889
 
5.4%
s 673
 
4.1%
t 645
 
3.9%
Other values (22) 4255
26.0%
Decimal Number
ValueCountFrequency (%)
1 20
26.7%
0 14
18.7%
4 13
17.3%
5 13
17.3%
2 5
 
6.7%
3 4
 
5.3%
8 4
 
5.3%
7 2
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 178
58.2%
, 99
32.4%
/ 17
 
5.6%
' 6
 
2.0%
: 4
 
1.3%
& 2
 
0.7%
Space Separator
ValueCountFrequency (%)
1913
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16390
85.6%
Common 2382
 
12.4%
Han 379
 
2.0%

Most frequent character per script

Han
ValueCountFrequency (%)
咖 47
 
12.4%
啡 45
 
11.9%
園 22
 
5.8%
莊 15
 
4.0%
山 14
 
3.7%
台 7
 
1.8%
坊 6
 
1.6%
里 5
 
1.3%
國 5
 
1.3%
高 5
 
1.3%
Other values (129) 208
54.9%
Latin
ValueCountFrequency (%)
a 2230
13.6%
e 1775
10.8%
i 1434
 
8.7%
o 1292
 
7.9%
c 1156
 
7.1%
n 1101
 
6.7%
l 940
 
5.7%
r 889
 
5.4%
s 673
 
4.1%
t 645
 
3.9%
Other values (22) 4255
26.0%
Common
ValueCountFrequency (%)
1913
80.3%
. 178
 
7.5%
, 99
 
4.2%
- 34
 
1.4%
) 26
 
1.1%
( 26
 
1.1%
1 20
 
0.8%
/ 17
 
0.7%
0 14
 
0.6%
4 13
 
0.5%
Other values (9) 42
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18724
97.8%
CJK 379
 
2.0%
None 46
 
0.2%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2230
11.9%
1913
 
10.2%
e 1775
 
9.5%
i 1434
 
7.7%
o 1292
 
6.9%
c 1156
 
6.2%
n 1101
 
5.9%
l 940
 
5.0%
r 889
 
4.7%
s 673
 
3.6%
Other values (34) 5321
28.4%
CJK
ValueCountFrequency (%)
咖 47
 
12.4%
啡 45
 
11.9%
園 22
 
5.8%
莊 15
 
4.0%
山 14
 
3.7%
台 7
 
1.8%
坊 6
 
1.6%
里 5
 
1.3%
國 5
 
1.3%
高 5
 
1.3%
Other values (129) 208
54.9%
None
ValueCountFrequency (%)
é 17
37.0%
ñ 15
32.6%
ú 5
 
10.9%
ó 5
 
10.9%
á 2
 
4.3%
í 2
 
4.3%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

ICO.Number
Categorical

HIGH CARDINALITY  MISSING 

Distinct841
Distinct (%)72.2%
Missing146
Missing (%)11.1%
Memory size20.5 KiB
0
 
67
Taiwan
 
31
2222
 
11
-
 
9
002/4177/0150
 
7
Other values (836)
1040 

Length

Max length40
Median length34
Mean length9.0214592
Min length1

Characters and Unicode

Total characters10510
Distinct characters69
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique714 ?
Unique (%)61.3%

Sample

1st row2014/2015
2nd row2014/2015
3rd row2014/2015
4th row010/0338
5th row010/0338

Common Values

ValueCountFrequency (%)
0 67
 
5.1%
Taiwan 31
 
2.4%
2222 11
 
0.8%
- 9
 
0.7%
002/4177/0150 7
 
0.5%
Taiwan台灣 7
 
0.5%
002/1660/0105 7
 
0.5%
002/1660/0080 6
 
0.5%
1 6
 
0.5%
unknown 6
 
0.5%
Other values (831) 1008
76.9%
(Missing) 146
 
11.1%

Length

2023-07-24T18:57:10.621403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 67
 
5.2%
taiwan 31
 
2.4%
21
 
1.6%
hdoa 16
 
1.3%
2222 11
 
0.9%
none 10
 
0.8%
kona 9
 
0.7%
1 8
 
0.6%
002/4177/0150 7
 
0.5%
taiwan台灣 7
 
0.5%
Other values (873) 1092
85.4%

Most occurring characters

ValueCountFrequency (%)
0 1748
16.6%
1 1580
15.0%
2 940
8.9%
3 865
8.2%
- 755
 
7.2%
/ 626
 
6.0%
5 577
 
5.5%
6 516
 
4.9%
7 495
 
4.7%
4 480
 
4.6%
Other values (59) 1928
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7942
75.6%
Dash Punctuation 756
 
7.2%
Other Punctuation 655
 
6.2%
Lowercase Letter 593
 
5.6%
Uppercase Letter 416
 
4.0%
Space Separator 122
 
1.2%
Other Letter 16
 
0.2%
Format 4
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 114
19.2%
n 95
16.0%
i 83
14.0%
w 47
7.9%
e 44
 
7.4%
o 34
 
5.7%
c 24
 
4.0%
d 19
 
3.2%
s 17
 
2.9%
f 17
 
2.9%
Other values (13) 99
16.7%
Uppercase Letter
ValueCountFrequency (%)
C 58
13.9%
T 50
12.0%
A 47
11.3%
O 41
9.9%
K 33
7.9%
P 29
7.0%
N 26
 
6.2%
D 22
 
5.3%
F 22
 
5.3%
H 17
 
4.1%
Other values (12) 71
17.1%
Decimal Number
ValueCountFrequency (%)
0 1748
22.0%
1 1580
19.9%
2 940
11.8%
3 865
10.9%
5 577
 
7.3%
6 516
 
6.5%
7 495
 
6.2%
4 480
 
6.0%
8 378
 
4.8%
9 363
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/ 626
95.6%
, 12
 
1.8%
# 7
 
1.1%
. 6
 
0.9%
? 2
 
0.3%
; 2
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 755
99.9%
– 1
 
0.1%
Other Letter
ValueCountFrequency (%)
灣 8
50.0%
台 8
50.0%
Space Separator
ValueCountFrequency (%)
122
100.0%
Format
ValueCountFrequency (%)
⁠ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9485
90.2%
Latin 1009
 
9.6%
Han 16
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 114
 
11.3%
n 95
 
9.4%
i 83
 
8.2%
C 58
 
5.7%
T 50
 
5.0%
w 47
 
4.7%
A 47
 
4.7%
e 44
 
4.4%
O 41
 
4.1%
o 34
 
3.4%
Other values (35) 396
39.2%
Common
ValueCountFrequency (%)
0 1748
18.4%
1 1580
16.7%
2 940
9.9%
3 865
9.1%
- 755
8.0%
/ 626
 
6.6%
5 577
 
6.1%
6 516
 
5.4%
7 495
 
5.2%
4 480
 
5.1%
Other values (12) 903
9.5%
Han
ValueCountFrequency (%)
灣 8
50.0%
台 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10489
99.8%
CJK 16
 
0.2%
Punctuation 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1748
16.7%
1 1580
15.1%
2 940
9.0%
3 865
8.2%
- 755
 
7.2%
/ 626
 
6.0%
5 577
 
5.5%
6 516
 
4.9%
7 495
 
4.7%
4 480
 
4.6%
Other values (55) 1907
18.2%
CJK
ValueCountFrequency (%)
灣 8
50.0%
台 8
50.0%
Punctuation
ValueCountFrequency (%)
⁠ 4
80.0%
– 1
 
20.0%

Company
Categorical

HIGH CARDINALITY  MISSING 

Distinct270
Distinct (%)24.5%
Missing209
Missing (%)15.9%
Memory size20.5 KiB
unex guatemala, s.a.
86 
ipanema coffees
 
50
racafe & cia s.c.a
 
40
exportadora de cafe condor s.a
 
40
kona pacific farmers cooperative
 
40
Other values (265)
846 

Length

Max length78
Median length50
Mean length21.233212
Min length3

Characters and Unicode

Total characters23399
Distinct characters61
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)12.6%

Sample

1st rowmetad agricultural developmet plc
2nd rowmetad agricultural developmet plc
3rd rowyidnekachew debessa coffee plantation
4th rowmetad agricultural developmet plc
5th rowrichmond investment-coffee department

Common Values

ValueCountFrequency (%)
unex guatemala, s.a. 86
 
6.6%
ipanema coffees 50
 
3.8%
racafe & cia s.c.a 40
 
3.1%
exportadora de cafe condor s.a 40
 
3.1%
kona pacific farmers cooperative 40
 
3.1%
blossom valley宸嶧國際 25
 
1.9%
carcafe ltda 25
 
1.9%
nucoffee 24
 
1.8%
taiwan coffee laboratory 20
 
1.5%
宸嶧國際 19
 
1.4%
Other values (260) 733
55.9%
(Missing) 209
 
15.9%

Length

2023-07-24T18:57:11.246412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.a 246
 
6.8%
de 215
 
6.0%
coffee 181
 
5.0%
guatemala 104
 
2.9%
unex 86
 
2.4%
ltd 81
 
2.2%
exportadora 70
 
1.9%
coffees 69
 
1.9%
cafe 68
 
1.9%
64
 
1.8%
Other values (505) 2429
67.2%

Most occurring characters

ValueCountFrequency (%)
a 2831
12.1%
2519
 
10.8%
e 2339
 
10.0%
c 1653
 
7.1%
o 1581
 
6.8%
s 1162
 
5.0%
r 1142
 
4.9%
n 1071
 
4.6%
i 1032
 
4.4%
f 1029
 
4.4%
Other values (51) 7040
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19504
83.4%
Space Separator 2519
 
10.8%
Other Punctuation 1136
 
4.9%
Other Letter 183
 
0.8%
Open Punctuation 17
 
0.1%
Close Punctuation 17
 
0.1%
Decimal Number 15
 
0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2831
14.5%
e 2339
12.0%
c 1653
 
8.5%
o 1581
 
8.1%
s 1162
 
6.0%
r 1142
 
5.9%
n 1071
 
5.5%
i 1032
 
5.3%
f 1029
 
5.3%
t 984
 
5.0%
Other values (23) 4680
24.0%
Other Letter
ValueCountFrequency (%)
宸 44
24.0%
嶧 44
24.0%
國 44
24.0%
際 44
24.0%
王 1
 
0.5%
秋 1
 
0.5%
金 1
 
0.5%
八 1
 
0.5%
杞 1
 
0.5%
莊 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 4
26.7%
3 3
20.0%
1 2
13.3%
7 2
13.3%
5 1
 
6.7%
9 1
 
6.7%
8 1
 
6.7%
2 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 889
78.3%
, 182
 
16.0%
& 58
 
5.1%
/ 5
 
0.4%
' 2
 
0.2%
Space Separator
ValueCountFrequency (%)
2519
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19504
83.4%
Common 3712
 
15.9%
Han 183
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2831
14.5%
e 2339
12.0%
c 1653
 
8.5%
o 1581
 
8.1%
s 1162
 
6.0%
r 1142
 
5.9%
n 1071
 
5.5%
i 1032
 
5.3%
f 1029
 
5.3%
t 984
 
5.0%
Other values (23) 4680
24.0%
Common
ValueCountFrequency (%)
2519
67.9%
. 889
 
23.9%
, 182
 
4.9%
& 58
 
1.6%
( 17
 
0.5%
) 17
 
0.5%
- 8
 
0.2%
/ 5
 
0.1%
0 4
 
0.1%
3 3
 
0.1%
Other values (7) 10
 
0.3%
Han
ValueCountFrequency (%)
宸 44
24.0%
嶧 44
24.0%
國 44
24.0%
際 44
24.0%
王 1
 
0.5%
秋 1
 
0.5%
金 1
 
0.5%
八 1
 
0.5%
杞 1
 
0.5%
莊 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23144
98.9%
CJK 183
 
0.8%
None 72
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2831
12.2%
2519
 
10.9%
e 2339
 
10.1%
c 1653
 
7.1%
o 1581
 
6.8%
s 1162
 
5.0%
r 1142
 
4.9%
n 1071
 
4.6%
i 1032
 
4.5%
f 1029
 
4.4%
Other values (33) 6785
29.3%
CJK
ValueCountFrequency (%)
宸 44
24.0%
嶧 44
24.0%
國 44
24.0%
際 44
24.0%
王 1
 
0.5%
秋 1
 
0.5%
金 1
 
0.5%
八 1
 
0.5%
杞 1
 
0.5%
莊 1
 
0.5%
None
ValueCountFrequency (%)
é 43
59.7%
ñ 13
 
18.1%
ó 8
 
11.1%
í 3
 
4.2%
á 2
 
2.8%
ú 2
 
2.8%
è 1
 
1.4%

Altitude
Categorical

HIGH CARDINALITY  MISSING 

Distinct383
Distinct (%)35.2%
Missing223
Missing (%)17.0%
Memory size20.5 KiB
1100
 
43
1200
 
41
1300
 
32
1400
 
32
4300
 
31
Other values (378)
909 

Length

Max length41
Median length4
Mean length6.3943015
Min length1

Characters and Unicode

Total characters6957
Distinct characters42
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)22.2%

Sample

1st row1950-2200
2nd row1950-2200
3rd row1600 - 1800 m
4th row1800-2200
5th row1950-2200

Common Values

ValueCountFrequency (%)
1100 43
 
3.3%
1200 41
 
3.1%
1300 32
 
2.4%
1400 32
 
2.4%
4300 31
 
2.4%
1500 30
 
2.3%
1250 30
 
2.3%
1700 28
 
2.1%
1550 24
 
1.8%
1800 22
 
1.7%
Other values (373) 775
59.1%
(Missing) 223
 
17.0%

Length

2023-07-24T18:57:11.496402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
msnm 105
 
6.7%
m 60
 
3.8%
1200 58
 
3.7%
1600 55
 
3.5%
1400 52
 
3.3%
1100 47
 
3.0%
1300 44
 
2.8%
1500 42
 
2.7%
1800 40
 
2.5%
a 36
 
2.3%
Other values (317) 1037
65.8%

Most occurring characters

ValueCountFrequency (%)
0 1969
28.3%
1 1019
14.6%
488
 
7.0%
m 485
 
7.0%
5 461
 
6.6%
4 268
 
3.9%
2 244
 
3.5%
s 226
 
3.2%
8 174
 
2.5%
3 161
 
2.3%
Other values (32) 1462
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4734
68.0%
Lowercase Letter 1463
 
21.0%
Space Separator 488
 
7.0%
Other Punctuation 118
 
1.7%
Dash Punctuation 111
 
1.6%
Math Symbol 26
 
0.4%
Other Letter 16
 
0.2%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 485
33.2%
s 226
15.4%
e 161
 
11.0%
n 151
 
10.3%
a 84
 
5.7%
t 81
 
5.5%
r 48
 
3.3%
l 47
 
3.2%
o 36
 
2.5%
d 35
 
2.4%
Other values (11) 109
 
7.5%
Decimal Number
ValueCountFrequency (%)
0 1969
41.6%
1 1019
21.5%
5 461
 
9.7%
4 268
 
5.7%
2 244
 
5.2%
8 174
 
3.7%
3 161
 
3.4%
6 159
 
3.4%
9 142
 
3.0%
7 137
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 109
92.4%
: 6
 
5.1%
' 2
 
1.7%
/ 1
 
0.8%
Math Symbol
ValueCountFrequency (%)
+ 22
84.6%
~ 4
 
15.4%
Other Letter
ValueCountFrequency (%)
公 8
50.0%
尺 8
50.0%
Space Separator
ValueCountFrequency (%)
488
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5478
78.7%
Latin 1463
 
21.0%
Han 16
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 485
33.2%
s 226
15.4%
e 161
 
11.0%
n 151
 
10.3%
a 84
 
5.7%
t 81
 
5.5%
r 48
 
3.3%
l 47
 
3.2%
o 36
 
2.5%
d 35
 
2.4%
Other values (11) 109
 
7.5%
Common
ValueCountFrequency (%)
0 1969
35.9%
1 1019
18.6%
488
 
8.9%
5 461
 
8.4%
4 268
 
4.9%
2 244
 
4.5%
8 174
 
3.2%
3 161
 
2.9%
6 159
 
2.9%
9 142
 
2.6%
Other values (9) 393
 
7.2%
Han
ValueCountFrequency (%)
公 8
50.0%
尺 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6941
99.8%
CJK 16
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1969
28.4%
1 1019
14.7%
488
 
7.0%
m 485
 
7.0%
5 461
 
6.6%
4 268
 
3.9%
2 244
 
3.5%
s 226
 
3.3%
8 174
 
2.5%
3 161
 
2.3%
Other values (30) 1446
20.8%
CJK
ValueCountFrequency (%)
公 8
50.0%
尺 8
50.0%

Region
Categorical

HIGH CARDINALITY  MISSING 

Distinct343
Distinct (%)27.4%
Missing57
Missing (%)4.3%
Memory size20.5 KiB
huila
112 
oriente
 
80
south of minas
 
68
kona
 
66
veracruz
 
35
Other values (338)
893 

Length

Max length76
Median length46
Mean length10.848485
Min length2

Characters and Unicode

Total characters13604
Distinct characters104
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)15.2%

Sample

1st rowguji-hambela
2nd rowguji-hambela
3rd roworomia
4th rowguji-hambela
5th roworomia

Common Values

ValueCountFrequency (%)
huila 112
 
8.5%
oriente 80
 
6.1%
south of minas 68
 
5.2%
kona 66
 
5.0%
veracruz 35
 
2.7%
tarrazu 19
 
1.4%
comayagua 17
 
1.3%
huehuetenango 16
 
1.2%
san marcos 16
 
1.2%
antigua 15
 
1.1%
Other values (333) 810
61.8%
(Missing) 57
 
4.3%

Length

2023-07-24T18:57:11.730775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
huila 114
 
5.4%
oriente 89
 
4.2%
minas 88
 
4.2%
of 73
 
3.5%
south 71
 
3.4%
kona 66
 
3.1%
de 41
 
1.9%
san 38
 
1.8%
veracruz 36
 
1.7%
chiapas 35
 
1.7%
Other values (476) 1453
69.1%

Most occurring characters

ValueCountFrequency (%)
a 2039
15.0%
n 1046
 
7.7%
o 968
 
7.1%
i 945
 
6.9%
e 928
 
6.8%
866
 
6.4%
t 771
 
5.7%
r 680
 
5.0%
u 663
 
4.9%
c 572
 
4.2%
Other values (94) 4126
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12246
90.0%
Space Separator 866
 
6.4%
Other Letter 294
 
2.2%
Other Punctuation 159
 
1.2%
Dash Punctuation 15
 
0.1%
Decimal Number 9
 
0.1%
Open Punctuation 8
 
0.1%
Close Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
市 31
 
10.5%
山 27
 
9.2%
區 26
 
8.8%
南 19
 
6.5%
東 17
 
5.8%
台 16
 
5.4%
臺 13
 
4.4%
中 10
 
3.4%
新 8
 
2.7%
鄉 8
 
2.7%
Other values (46) 119
40.5%
Lowercase Letter
ValueCountFrequency (%)
a 2039
16.7%
n 1046
 
8.5%
o 968
 
7.9%
i 945
 
7.7%
e 928
 
7.6%
t 771
 
6.3%
r 680
 
5.6%
u 663
 
5.4%
c 572
 
4.7%
l 561
 
4.6%
Other values (21) 3073
25.1%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
2 1
 
11.1%
5 1
 
11.1%
3 1
 
11.1%
8 1
 
11.1%
6 1
 
11.1%
0 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 127
79.9%
. 24
 
15.1%
; 4
 
2.5%
: 2
 
1.3%
/ 1
 
0.6%
' 1
 
0.6%
Space Separator
ValueCountFrequency (%)
866
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12246
90.0%
Common 1064
 
7.8%
Han 294
 
2.2%

Most frequent character per script

Han
ValueCountFrequency (%)
市 31
 
10.5%
山 27
 
9.2%
區 26
 
8.8%
南 19
 
6.5%
東 17
 
5.8%
台 16
 
5.4%
臺 13
 
4.4%
中 10
 
3.4%
新 8
 
2.7%
鄉 8
 
2.7%
Other values (46) 119
40.5%
Latin
ValueCountFrequency (%)
a 2039
16.7%
n 1046
 
8.5%
o 968
 
7.9%
i 945
 
7.7%
e 928
 
7.6%
t 771
 
6.3%
r 680
 
5.6%
u 663
 
5.4%
c 572
 
4.7%
l 561
 
4.6%
Other values (21) 3073
25.1%
Common
ValueCountFrequency (%)
866
81.4%
, 127
 
11.9%
. 24
 
2.3%
- 15
 
1.4%
( 8
 
0.8%
) 7
 
0.7%
; 4
 
0.4%
1 3
 
0.3%
: 2
 
0.2%
/ 1
 
0.1%
Other values (7) 7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13296
97.7%
CJK 294
 
2.2%
None 14
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2039
15.3%
n 1046
 
7.9%
o 968
 
7.3%
i 945
 
7.1%
e 928
 
7.0%
866
 
6.5%
t 771
 
5.8%
r 680
 
5.1%
u 663
 
5.0%
c 572
 
4.3%
Other values (33) 3818
28.7%
CJK
ValueCountFrequency (%)
市 31
 
10.5%
山 27
 
9.2%
區 26
 
8.8%
南 19
 
6.5%
東 17
 
5.8%
台 16
 
5.4%
臺 13
 
4.4%
中 10
 
3.4%
新 8
 
2.7%
鄉 8
 
2.7%
Other values (46) 119
40.5%
None
ValueCountFrequency (%)
í 4
28.6%
ñ 4
28.6%
ó 3
21.4%
ã 2
14.3%
ú 1
 
7.1%

Producer
Categorical

HIGH CARDINALITY  MISSING 

Distinct675
Distinct (%)62.4%
Missing230
Missing (%)17.5%
Memory size20.5 KiB
La Plata
 
30
Ipanema Agrícola SA
 
22
Doi Tung Development Project
 
17
Ipanema Agricola
 
12
VARIOS
 
12
Other values (670)
988 

Length

Max length100
Median length58
Mean length20.540241
Min length1

Characters and Unicode

Total characters22204
Distinct characters222
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)48.7%

Sample

1st rowMETAD PLC
2nd rowMETAD PLC
3rd rowYidnekachew Dabessa Coffee Plantation
4th rowMETAD PLC
5th rowHVC

Common Values

ValueCountFrequency (%)
La Plata 30
 
2.3%
Ipanema Agrícola SA 22
 
1.7%
Doi Tung Development Project 17
 
1.3%
Ipanema Agricola 12
 
0.9%
VARIOS 12
 
0.9%
Ipanema Agricola S.A 11
 
0.8%
ROBERTO MONTERROSO 10
 
0.8%
AMILCAR LAPOLA 9
 
0.7%
Reinerio Zepeda 9
 
0.7%
LA PLATA 9
 
0.7%
Other values (665) 940
71.7%
(Missing) 230
 
17.5%

Length

2023-07-24T18:57:11.981247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 86
 
2.5%
65
 
1.9%
coffee 64
 
1.9%
la 60
 
1.8%
ipanema 50
 
1.5%
s.a 50
 
1.5%
plata 41
 
1.2%
agricola 37
 
1.1%
ltd 33
 
1.0%
sa 30
 
0.9%
Other values (1241) 2902
84.9%

Most occurring characters

ValueCountFrequency (%)
2392
 
10.8%
A 1473
 
6.6%
a 1174
 
5.3%
R 962
 
4.3%
E 961
 
4.3%
e 902
 
4.1%
O 886
 
4.0%
o 824
 
3.7%
I 758
 
3.4%
L 674
 
3.0%
Other values (212) 11198
50.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10990
49.5%
Lowercase Letter 8057
36.3%
Space Separator 2392
 
10.8%
Other Punctuation 373
 
1.7%
Other Letter 233
 
1.0%
Decimal Number 99
 
0.4%
Dash Punctuation 17
 
0.1%
Open Punctuation 14
 
0.1%
Close Punctuation 14
 
0.1%
Math Symbol 12
 
0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
林 12
 
5.2%
張 7
 
3.0%
曾 6
 
2.6%
政 5
 
2.1%
黃 5
 
2.1%
倫 5
 
2.1%
俊 5
 
2.1%
楓 4
 
1.7%
文 4
 
1.7%
郭 4
 
1.7%
Other values (122) 176
75.5%
Lowercase Letter
ValueCountFrequency (%)
a 1174
14.6%
e 902
11.2%
o 824
10.2%
n 608
 
7.5%
r 607
 
7.5%
i 554
 
6.9%
l 392
 
4.9%
t 392
 
4.9%
s 328
 
4.1%
u 322
 
4.0%
Other values (24) 1954
24.3%
Uppercase Letter
ValueCountFrequency (%)
A 1473
13.4%
R 962
 
8.8%
E 961
 
8.7%
O 886
 
8.1%
I 758
 
6.9%
L 674
 
6.1%
N 620
 
5.6%
S 610
 
5.6%
C 601
 
5.5%
M 430
 
3.9%
Other values (22) 3015
27.4%
Decimal Number
ValueCountFrequency (%)
2 20
20.2%
0 20
20.2%
1 16
16.2%
3 12
12.1%
9 11
11.1%
4 8
 
8.1%
6 5
 
5.1%
7 3
 
3.0%
8 2
 
2.0%
5 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 195
52.3%
, 95
25.5%
/ 53
 
14.2%
& 13
 
3.5%
' 6
 
1.6%
: 6
 
1.6%
; 5
 
1.3%
Space Separator
ValueCountFrequency (%)
2392
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Math Symbol
ValueCountFrequency (%)
| 12
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19047
85.8%
Common 2924
 
13.2%
Han 233
 
1.0%

Most frequent character per script

Han
ValueCountFrequency (%)
林 12
 
5.2%
張 7
 
3.0%
曾 6
 
2.6%
政 5
 
2.1%
黃 5
 
2.1%
倫 5
 
2.1%
俊 5
 
2.1%
楓 4
 
1.7%
文 4
 
1.7%
郭 4
 
1.7%
Other values (122) 176
75.5%
Latin
ValueCountFrequency (%)
A 1473
 
7.7%
a 1174
 
6.2%
R 962
 
5.1%
E 961
 
5.0%
e 902
 
4.7%
O 886
 
4.7%
o 824
 
4.3%
I 758
 
4.0%
L 674
 
3.5%
N 620
 
3.3%
Other values (56) 9813
51.5%
Common
ValueCountFrequency (%)
2392
81.8%
. 195
 
6.7%
, 95
 
3.2%
/ 53
 
1.8%
2 20
 
0.7%
0 20
 
0.7%
- 17
 
0.6%
1 16
 
0.5%
( 14
 
0.5%
) 14
 
0.5%
Other values (14) 88
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21867
98.5%
CJK 233
 
1.0%
None 102
 
0.5%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2392
 
10.9%
A 1473
 
6.7%
a 1174
 
5.4%
R 962
 
4.4%
E 961
 
4.4%
e 902
 
4.1%
O 886
 
4.1%
o 824
 
3.8%
I 758
 
3.5%
L 674
 
3.1%
Other values (65) 10861
49.7%
None
ValueCountFrequency (%)
í 25
24.5%
é 13
12.7%
ó 12
11.8%
Ñ 10
 
9.8%
Í 9
 
8.8%
ú 7
 
6.9%
á 6
 
5.9%
É 4
 
3.9%
Ó 4
 
3.9%
è 4
 
3.9%
Other values (4) 8
 
7.8%
CJK
ValueCountFrequency (%)
林 12
 
5.2%
張 7
 
3.0%
曾 6
 
2.6%
政 5
 
2.1%
黃 5
 
2.1%
倫 5
 
2.1%
俊 5
 
2.1%
楓 4
 
1.7%
文 4
 
1.7%
郭 4
 
1.7%
Other values (122) 176
75.5%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Number.of.Bags
Real number (ℝ)

Distinct130
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.88787
Minimum0
Maximum1062
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:12.200002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114.5
median175
Q3275
95-th percentile320
Maximum1062
Range1062
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation129.73373
Coefficient of variation (CV)0.84304066
Kurtosis0.28313562
Mean153.88787
Median Absolute Deviation (MAD)125
Skewness0.32359511
Sum201747
Variance16830.842
MonotonicityNot monotonic
2023-07-24T18:57:12.418747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 241
18.4%
275 176
13.4%
10 108
 
8.2%
1 87
 
6.6%
300 71
 
5.4%
320 70
 
5.3%
50 42
 
3.2%
100 37
 
2.8%
20 35
 
2.7%
2 29
 
2.2%
Other values (120) 415
31.7%
ValueCountFrequency (%)
0 1
 
0.1%
1 87
6.6%
2 29
 
2.2%
3 18
 
1.4%
4 6
 
0.5%
5 14
 
1.1%
6 9
 
0.7%
7 8
 
0.6%
8 10
 
0.8%
9 1
 
0.1%
ValueCountFrequency (%)
1062 1
 
0.1%
600 1
 
0.1%
550 2
0.2%
500 2
0.2%
450 2
0.2%
440 3
0.2%
400 1
 
0.1%
380 1
 
0.1%
377 1
 
0.1%
360 4
0.3%

Bag.Weight
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct56
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
1 kg
327 
60 kg
242 
69 kg
200 
70 kg
156 
2 kg
114 
Other values (51)
272 

Length

Max length8
Median length5
Mean length4.7032799
Min length1

Characters and Unicode

Total characters6166
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)1.8%

Sample

1st row60 kg
2nd row60 kg
3rd row1
4th row60 kg
5th row60 kg

Common Values

ValueCountFrequency (%)
1 kg 327
24.9%
60 kg 242
18.5%
69 kg 200
15.3%
70 kg 156
11.9%
2 kg 114
 
8.7%
100 lbs 59
 
4.5%
30 kg 29
 
2.2%
5 lbs 21
 
1.6%
6 19
 
1.4%
20 kg 14
 
1.1%
Other values (46) 130
 
9.9%

Length

2023-07-24T18:57:12.606250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg 1170
45.1%
1 343
 
13.2%
60 242
 
9.3%
69 200
 
7.7%
70 156
 
6.0%
2 121
 
4.7%
lbs 112
 
4.3%
100 60
 
2.3%
30 29
 
1.1%
5 28
 
1.1%
Other values (36) 134
 
5.2%

Most occurring characters

ValueCountFrequency (%)
1284
20.8%
k 1172
19.0%
g 1172
19.0%
0 624
10.1%
6 473
 
7.7%
1 433
 
7.0%
9 220
 
3.6%
7 161
 
2.6%
2 146
 
2.4%
l 114
 
1.8%
Other values (7) 367
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2686
43.6%
Decimal Number 2194
35.6%
Space Separator 1284
20.8%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 624
28.4%
6 473
21.6%
1 433
19.7%
9 220
 
10.0%
7 161
 
7.3%
2 146
 
6.7%
5 67
 
3.1%
3 44
 
2.0%
8 14
 
0.6%
4 12
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
k 1172
43.6%
g 1172
43.6%
l 114
 
4.2%
b 114
 
4.2%
s 114
 
4.2%
Space Separator
ValueCountFrequency (%)
1284
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3480
56.4%
Latin 2686
43.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1284
36.9%
0 624
17.9%
6 473
 
13.6%
1 433
 
12.4%
9 220
 
6.3%
7 161
 
4.6%
2 146
 
4.2%
5 67
 
1.9%
3 44
 
1.3%
8 14
 
0.4%
Other values (2) 14
 
0.4%
Latin
ValueCountFrequency (%)
k 1172
43.6%
g 1172
43.6%
l 114
 
4.2%
b 114
 
4.2%
s 114
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1284
20.8%
k 1172
19.0%
g 1172
19.0%
0 624
10.1%
6 473
 
7.7%
1 433
 
7.0%
9 220
 
3.6%
7 161
 
2.6%
2 146
 
2.4%
l 114
 
1.8%
Other values (7) 367
 
6.0%

In.Country.Partner
Categorical

Distinct27
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
Specialty Coffee Association
295 
AMECAFE
205 
Almacafé
178 
Asociacion Nacional Del Café
155 
Brazil Specialty Coffee Association
67 
Other values (22)
411 

Length

Max length85
Median length62
Mean length22.929825
Min length7

Characters and Unicode

Total characters30061
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowMETAD Agricultural Development plc
2nd rowMETAD Agricultural Development plc
3rd rowSpecialty Coffee Association
4th rowMETAD Agricultural Development plc
5th rowMETAD Agricultural Development plc

Common Values

ValueCountFrequency (%)
Specialty Coffee Association 295
22.5%
AMECAFE 205
15.6%
Almacafé 178
13.6%
Asociacion Nacional Del Café 155
11.8%
Brazil Specialty Coffee Association 67
 
5.1%
Instituto Hondureño del Café 60
 
4.6%
Blossom Valley International 58
 
4.4%
Africa Fine Coffee Association 49
 
3.7%
Specialty Coffee Association of Costa Rica 42
 
3.2%
NUCOFFEE 36
 
2.7%
Other values (17) 166
12.7%

Length

2023-07-24T18:57:12.809374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coffee 562
15.0%
association 485
12.9%
specialty 431
11.5%
café 225
 
6.0%
del 224
 
6.0%
amecafe 205
 
5.5%
almacafé 178
 
4.7%
asociacion 155
 
4.1%
nacional 155
 
4.1%
of 68
 
1.8%
Other values (52) 1065
28.4%

Most occurring characters

ValueCountFrequency (%)
a 2701
 
9.0%
o 2668
 
8.9%
2442
 
8.1%
i 2428
 
8.1%
e 2314
 
7.7%
c 1799
 
6.0%
f 1664
 
5.5%
t 1481
 
4.9%
s 1476
 
4.9%
l 1426
 
4.7%
Other values (43) 9662
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22455
74.7%
Uppercase Letter 5133
 
17.1%
Space Separator 2442
 
8.1%
Other Punctuation 29
 
0.1%
Dash Punctuation 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2701
12.0%
o 2668
11.9%
i 2428
10.8%
e 2314
10.3%
c 1799
8.0%
f 1664
7.4%
t 1481
6.6%
s 1476
6.6%
l 1426
6.4%
n 1409
6.3%
Other values (17) 3089
13.8%
Uppercase Letter
ValueCountFrequency (%)
A 1382
26.9%
C 1145
22.3%
E 559
10.9%
S 442
 
8.6%
F 326
 
6.4%
M 226
 
4.4%
D 222
 
4.3%
N 199
 
3.9%
I 152
 
3.0%
B 132
 
2.6%
Other values (11) 348
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 28
96.6%
& 1
 
3.4%
Space Separator
ValueCountFrequency (%)
2442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27588
91.8%
Common 2473
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2701
 
9.8%
o 2668
 
9.7%
i 2428
 
8.8%
e 2314
 
8.4%
c 1799
 
6.5%
f 1664
 
6.0%
t 1481
 
5.4%
s 1476
 
5.4%
l 1426
 
5.2%
n 1409
 
5.1%
Other values (38) 8222
29.8%
Common
ValueCountFrequency (%)
2442
98.7%
. 28
 
1.1%
- 1
 
< 0.1%
& 1
 
< 0.1%
1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29555
98.3%
None 506
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2701
 
9.1%
o 2668
 
9.0%
2442
 
8.3%
i 2428
 
8.2%
e 2314
 
7.8%
c 1799
 
6.1%
f 1664
 
5.6%
t 1481
 
5.0%
s 1476
 
5.0%
l 1426
 
4.8%
Other values (38) 9156
31.0%
None
ValueCountFrequency (%)
é 417
82.4%
ñ 60
 
11.9%
ó 22
 
4.3%
í 6
 
1.2%
ú 1
 
0.2%

Harvest.Year
Categorical

Distinct46
Distinct (%)3.6%
Missing47
Missing (%)3.6%
Memory size20.5 KiB
2012
352 
2014
226 
2013
170 
2015
125 
2016
122 
Other values (41)
269 

Length

Max length24
Median length4
Mean length4.7381329
Min length3

Characters and Unicode

Total characters5989
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.7%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2013

Common Values

ValueCountFrequency (%)
2012 352
26.8%
2014 226
17.2%
2013 170
13.0%
2015 125
 
9.5%
2016 122
 
9.3%
2017 68
 
5.2%
2013/2014 29
 
2.2%
2015/2016 28
 
2.1%
2011 26
 
2.0%
2017 / 2018 19
 
1.4%
Other values (36) 99
 
7.6%
(Missing) 47
 
3.6%

Length

2023-07-24T18:57:12.996873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012 352
26.0%
2014 226
16.7%
2013 170
12.6%
2016 128
 
9.5%
2015 125
 
9.2%
2017 93
 
6.9%
31
 
2.3%
2011 30
 
2.2%
2013/2014 29
 
2.1%
2015/2016 28
 
2.1%
Other values (40) 142
10.5%

Most occurring characters

ValueCountFrequency (%)
2 1732
28.9%
0 1450
24.2%
1 1405
23.5%
4 285
 
4.8%
3 201
 
3.4%
5 172
 
2.9%
6 157
 
2.6%
/ 133
 
2.2%
7 96
 
1.6%
90
 
1.5%
Other values (30) 268
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5542
92.5%
Lowercase Letter 161
 
2.7%
Other Punctuation 134
 
2.2%
Space Separator 90
 
1.5%
Uppercase Letter 46
 
0.8%
Dash Punctuation 16
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19
11.8%
r 18
11.2%
l 15
9.3%
u 15
9.3%
o 15
9.3%
i 14
 
8.7%
c 8
 
5.0%
y 8
 
5.0%
e 7
 
4.3%
t 7
 
4.3%
Other values (7) 35
21.7%
Decimal Number
ValueCountFrequency (%)
2 1732
31.3%
0 1450
26.2%
1 1405
25.4%
4 285
 
5.1%
3 201
 
3.6%
5 172
 
3.1%
6 157
 
2.8%
7 96
 
1.7%
8 22
 
0.4%
9 22
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
T 13
28.3%
J 10
21.7%
M 8
17.4%
A 7
15.2%
S 3
 
6.5%
D 2
 
4.3%
E 1
 
2.2%
C 1
 
2.2%
F 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 133
99.3%
. 1
 
0.7%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5782
96.5%
Latin 207
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19
 
9.2%
r 18
 
8.7%
l 15
 
7.2%
u 15
 
7.2%
o 15
 
7.2%
i 14
 
6.8%
T 13
 
6.3%
J 10
 
4.8%
c 8
 
3.9%
M 8
 
3.9%
Other values (16) 72
34.8%
Common
ValueCountFrequency (%)
2 1732
30.0%
0 1450
25.1%
1 1405
24.3%
4 285
 
4.9%
3 201
 
3.5%
5 172
 
3.0%
6 157
 
2.7%
/ 133
 
2.3%
7 96
 
1.7%
90
 
1.6%
Other values (4) 61
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1732
28.9%
0 1450
24.2%
1 1405
23.5%
4 285
 
4.8%
3 201
 
3.4%
5 172
 
2.9%
6 157
 
2.6%
/ 133
 
2.2%
7 96
 
1.6%
90
 
1.5%
Other values (30) 268
 
4.5%

Grading.Date
Categorical

Distinct558
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
July 11th, 2012
 
25
December 26th, 2013
 
24
June 6th, 2012
 
19
August 30th, 2012
 
18
July 26th, 2012
 
15
Other values (553)
1210 

Length

Max length20
Median length18
Mean length16.601831
Min length13

Characters and Unicode

Total characters21765
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique278 ?
Unique (%)21.2%

Sample

1st rowApril 4th, 2015
2nd rowApril 4th, 2015
3rd rowMay 31st, 2010
4th rowMarch 26th, 2015
5th rowApril 4th, 2015

Common Values

ValueCountFrequency (%)
July 11th, 2012 25
 
1.9%
December 26th, 2013 24
 
1.8%
June 6th, 2012 19
 
1.4%
August 30th, 2012 18
 
1.4%
July 26th, 2012 15
 
1.1%
October 8th, 2015 13
 
1.0%
September 27th, 2012 13
 
1.0%
March 29th, 2013 13
 
1.0%
June 17th, 2010 12
 
0.9%
October 20th, 2017 11
 
0.8%
Other values (548) 1148
87.6%

Length

2023-07-24T18:57:13.168752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012 314
 
8.0%
2014 246
 
6.3%
2015 195
 
5.0%
june 154
 
3.9%
2013 153
 
3.9%
2017 141
 
3.6%
july 127
 
3.2%
april 127
 
3.2%
may 125
 
3.2%
2016 123
 
3.1%
Other values (42) 2228
56.6%

Most occurring characters

ValueCountFrequency (%)
2622
 
12.0%
2 2185
 
10.0%
1 1975
 
9.1%
0 1479
 
6.8%
t 1423
 
6.5%
, 1311
 
6.0%
h 1143
 
5.3%
e 1070
 
4.9%
r 1010
 
4.6%
u 710
 
3.3%
Other values (30) 6837
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9025
41.5%
Decimal Number 7494
34.4%
Space Separator 2622
 
12.0%
Other Punctuation 1311
 
6.0%
Uppercase Letter 1311
 
6.0%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1423
15.8%
h 1143
12.7%
e 1070
11.9%
r 1010
11.2%
u 710
7.9%
a 547
 
6.1%
y 457
 
5.1%
b 449
 
5.0%
n 329
 
3.6%
c 286
 
3.2%
Other values (9) 1601
17.7%
Decimal Number
ValueCountFrequency (%)
2 2185
29.2%
1 1975
26.4%
0 1479
19.7%
3 381
 
5.1%
4 333
 
4.4%
6 329
 
4.4%
5 300
 
4.0%
7 277
 
3.7%
9 118
 
1.6%
8 117
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
J 380
29.0%
M 243
18.5%
A 239
18.2%
S 110
 
8.4%
F 106
 
8.1%
D 91
 
6.9%
O 77
 
5.9%
N 65
 
5.0%
Space Separator
ValueCountFrequency (%)
2622
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1311
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11429
52.5%
Latin 10336
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1423
13.8%
h 1143
11.1%
e 1070
 
10.4%
r 1010
 
9.8%
u 710
 
6.9%
a 547
 
5.3%
y 457
 
4.4%
b 449
 
4.3%
J 380
 
3.7%
n 329
 
3.2%
Other values (17) 2818
27.3%
Common
ValueCountFrequency (%)
2622
22.9%
2 2185
19.1%
1 1975
17.3%
0 1479
12.9%
, 1311
11.5%
3 381
 
3.3%
4 333
 
2.9%
6 329
 
2.9%
5 300
 
2.6%
7 277
 
2.4%
Other values (3) 237
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2622
 
12.0%
2 2185
 
10.0%
1 1975
 
9.1%
0 1479
 
6.8%
t 1423
 
6.5%
, 1311
 
6.0%
h 1143
 
5.3%
e 1070
 
4.9%
r 1010
 
4.6%
u 710
 
3.3%
Other values (30) 6837
31.4%

Owner.1
Categorical

Distinct309
Distinct (%)23.7%
Missing7
Missing (%)0.5%
Memory size20.5 KiB
Juan Luis Alvarado Romero
155 
Racafe & Cia S.C.A
 
60
Exportadora de Cafe Condor S.A
 
54
Kona Pacific Farmers Cooperative
 
52
Ipanema Coffees
 
50
Other values (304)
933 

Length

Max length50
Median length40
Mean length21.346626
Min length3

Characters and Unicode

Total characters27836
Distinct characters78
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)13.1%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowGrounds for Health Admin
4th rowYidnekachew Dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
Juan Luis Alvarado Romero 155
 
11.8%
Racafe & Cia S.C.A 60
 
4.6%
Exportadora de Cafe Condor S.A 54
 
4.1%
Kona Pacific Farmers Cooperative 52
 
4.0%
Ipanema Coffees 50
 
3.8%
CQI Taiwan ICP CQI台灣合作夥伴 46
 
3.5%
NUCOFFEE 29
 
2.2%
Lin, Che-Hao Krude 林哲豪 29
 
2.2%
CARCAFE LTDA CI 27
 
2.1%
The Coffee Source Inc. 23
 
1.8%
Other values (299) 779
59.4%

Length

2023-07-24T18:57:13.387497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis 167
 
3.9%
juan 160
 
3.8%
alvarado 155
 
3.6%
romero 155
 
3.6%
de 114
 
2.7%
s.a 100
 
2.3%
coffee 83
 
2.0%
cafe 72
 
1.7%
exportadora 70
 
1.6%
coffees 67
 
1.6%
Other values (643) 3113
73.1%

Most occurring characters

ValueCountFrequency (%)
2960
 
10.6%
a 2148
 
7.7%
o 1623
 
5.8%
e 1591
 
5.7%
A 1249
 
4.5%
C 1183
 
4.2%
r 1158
 
4.2%
i 966
 
3.5%
E 943
 
3.4%
n 936
 
3.4%
Other values (68) 13079
47.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13840
49.7%
Uppercase Letter 10049
36.1%
Space Separator 2960
 
10.6%
Other Punctuation 512
 
1.8%
Other Letter 404
 
1.5%
Dash Punctuation 43
 
0.2%
Open Punctuation 13
 
< 0.1%
Close Punctuation 13
 
< 0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2148
15.5%
o 1623
11.7%
e 1591
11.5%
r 1158
 
8.4%
i 966
 
7.0%
n 936
 
6.8%
d 567
 
4.1%
u 551
 
4.0%
f 519
 
3.8%
s 506
 
3.7%
Other values (21) 3275
23.7%
Uppercase Letter
ValueCountFrequency (%)
A 1249
12.4%
C 1183
11.8%
E 943
 
9.4%
R 804
 
8.0%
L 661
 
6.6%
I 653
 
6.5%
S 581
 
5.8%
O 549
 
5.5%
N 390
 
3.9%
D 378
 
3.8%
Other values (16) 2658
26.5%
Other Letter
ValueCountFrequency (%)
伴 47
11.6%
夥 47
11.6%
作 47
11.6%
合 47
11.6%
灣 47
11.6%
台 47
11.6%
豪 30
7.4%
哲 30
7.4%
林 30
7.4%
宏 8
 
2.0%
Other values (3) 24
5.9%
Other Punctuation
ValueCountFrequency (%)
. 377
73.6%
, 72
 
14.1%
& 63
 
12.3%
Space Separator
ValueCountFrequency (%)
2960
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23889
85.8%
Common 3543
 
12.7%
Han 404
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2148
 
9.0%
o 1623
 
6.8%
e 1591
 
6.7%
A 1249
 
5.2%
C 1183
 
5.0%
r 1158
 
4.8%
i 966
 
4.0%
E 943
 
3.9%
n 936
 
3.9%
R 804
 
3.4%
Other values (47) 11288
47.3%
Han
ValueCountFrequency (%)
伴 47
11.6%
夥 47
11.6%
作 47
11.6%
合 47
11.6%
灣 47
11.6%
台 47
11.6%
豪 30
7.4%
哲 30
7.4%
林 30
7.4%
宏 8
 
2.0%
Other values (3) 24
5.9%
Common
ValueCountFrequency (%)
2960
83.5%
. 377
 
10.6%
, 72
 
2.0%
& 63
 
1.8%
- 43
 
1.2%
( 13
 
0.4%
) 13
 
0.4%
2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27390
98.4%
CJK 404
 
1.5%
None 42
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2960
 
10.8%
a 2148
 
7.8%
o 1623
 
5.9%
e 1591
 
5.8%
A 1249
 
4.6%
C 1183
 
4.3%
r 1158
 
4.2%
i 966
 
3.5%
E 943
 
3.4%
n 936
 
3.4%
Other values (50) 12633
46.1%
CJK
ValueCountFrequency (%)
伴 47
11.6%
夥 47
11.6%
作 47
11.6%
合 47
11.6%
灣 47
11.6%
台 47
11.6%
豪 30
7.4%
哲 30
7.4%
林 30
7.4%
宏 8
 
2.0%
Other values (3) 24
5.9%
None
ValueCountFrequency (%)
ñ 23
54.8%
é 12
28.6%
á 3
 
7.1%
ú 2
 
4.8%
ó 2
 
4.8%

Variety
Categorical

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)2.6%
Missing201
Missing (%)15.3%
Memory size20.5 KiB
Caturra
256 
Bourbon
226 
Typica
211 
Other
108 
Catuai
74 
Other values (24)
235 

Length

Max length21
Median length19
Mean length7.0216216
Min length4

Characters and Unicode

Total characters7794
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.5%

Sample

1st rowOther
2nd rowBourbon
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Caturra 256
19.5%
Bourbon 226
17.2%
Typica 211
16.1%
Other 108
8.2%
Catuai 74
 
5.6%
Hawaiian Kona 44
 
3.4%
Yellow Bourbon 35
 
2.7%
Mundo Novo 33
 
2.5%
Catimor 20
 
1.5%
SL14 17
 
1.3%
Other values (19) 86
 
6.6%
(Missing) 201
15.3%

Length

2023-07-24T18:57:13.574998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bourbon 261
21.2%
caturra 256
20.8%
typica 211
17.1%
other 108
8.8%
catuai 74
 
6.0%
hawaiian 44
 
3.6%
kona 44
 
3.6%
yellow 35
 
2.8%
mundo 33
 
2.7%
novo 33
 
2.7%
Other values (25) 133
10.8%

Most occurring characters

ValueCountFrequency (%)
a 1174
15.1%
r 936
12.0%
o 731
 
9.4%
u 643
 
8.2%
t 468
 
6.0%
i 413
 
5.3%
n 398
 
5.1%
C 351
 
4.5%
b 267
 
3.4%
B 263
 
3.4%
Other values (36) 2150
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6318
81.1%
Uppercase Letter 1270
 
16.3%
Space Separator 122
 
1.6%
Decimal Number 84
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1174
18.6%
r 936
14.8%
o 731
11.6%
u 643
10.2%
t 468
 
7.4%
i 413
 
6.5%
n 398
 
6.3%
b 267
 
4.2%
c 235
 
3.7%
y 217
 
3.4%
Other values (13) 836
13.2%
Uppercase Letter
ValueCountFrequency (%)
C 351
27.6%
B 263
20.7%
T 211
16.6%
O 108
 
8.5%
S 45
 
3.5%
H 45
 
3.5%
K 44
 
3.5%
L 41
 
3.2%
M 40
 
3.1%
Y 37
 
2.9%
Other values (7) 85
 
6.7%
Decimal Number
ValueCountFrequency (%)
4 25
29.8%
1 21
25.0%
2 15
17.9%
8 15
17.9%
3 8
 
9.5%
Space Separator
ValueCountFrequency (%)
122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7588
97.4%
Common 206
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1174
15.5%
r 936
12.3%
o 731
 
9.6%
u 643
 
8.5%
t 468
 
6.2%
i 413
 
5.4%
n 398
 
5.2%
C 351
 
4.6%
b 267
 
3.5%
B 263
 
3.5%
Other values (30) 1944
25.6%
Common
ValueCountFrequency (%)
122
59.2%
4 25
 
12.1%
1 21
 
10.2%
2 15
 
7.3%
8 15
 
7.3%
3 8
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1174
15.1%
r 936
12.0%
o 731
 
9.4%
u 643
 
8.2%
t 468
 
6.0%
i 413
 
5.3%
n 398
 
5.1%
C 351
 
4.5%
b 267
 
3.4%
B 263
 
3.4%
Other values (36) 2150
27.6%

Processing.Method
Categorical

Distinct5
Distinct (%)0.4%
Missing152
Missing (%)11.6%
Memory size20.5 KiB
Washed / Wet
812 
Natural / Dry
251 
Semi-washed / Semi-pulped
 
56
Other
 
26
Pulped natural / honey
 
14

Length

Max length25
Median length12
Mean length12.808456
Min length5

Characters and Unicode

Total characters14845
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashed / Wet
2nd rowWashed / Wet
3rd rowNatural / Dry
4th rowWashed / Wet
5th rowNatural / Dry

Common Values

ValueCountFrequency (%)
Washed / Wet 812
61.9%
Natural / Dry 251
 
19.1%
Semi-washed / Semi-pulped 56
 
4.3%
Other 26
 
2.0%
Pulped natural / honey 14
 
1.1%
(Missing) 152
 
11.6%

Length

2023-07-24T18:57:13.762498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T18:57:13.949998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1133
32.9%
washed 812
23.6%
wet 812
23.6%
natural 265
 
7.7%
dry 251
 
7.3%
semi-washed 56
 
1.6%
semi-pulped 56
 
1.6%
other 26
 
0.8%
pulped 14
 
0.4%
honey 14
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2280
15.4%
e 1902
12.8%
W 1624
10.9%
a 1398
9.4%
/ 1133
7.6%
t 1103
7.4%
d 938
6.3%
h 908
 
6.1%
s 868
 
5.8%
r 542
 
3.7%
Other values (15) 2149
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9042
60.9%
Space Separator 2280
 
15.4%
Uppercase Letter 2278
 
15.3%
Other Punctuation 1133
 
7.6%
Dash Punctuation 112
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1902
21.0%
a 1398
15.5%
t 1103
12.2%
d 938
10.4%
h 908
10.0%
s 868
9.6%
r 542
 
6.0%
l 335
 
3.7%
u 335
 
3.7%
y 265
 
2.9%
Other values (6) 448
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
W 1624
71.3%
N 251
 
11.0%
D 251
 
11.0%
S 112
 
4.9%
O 26
 
1.1%
P 14
 
0.6%
Space Separator
ValueCountFrequency (%)
2280
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11320
76.3%
Common 3525
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1902
16.8%
W 1624
14.3%
a 1398
12.3%
t 1103
9.7%
d 938
8.3%
h 908
8.0%
s 868
7.7%
r 542
 
4.8%
l 335
 
3.0%
u 335
 
3.0%
Other values (12) 1367
12.1%
Common
ValueCountFrequency (%)
2280
64.7%
/ 1133
32.1%
- 112
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2280
15.4%
e 1902
12.8%
W 1624
10.9%
a 1398
9.4%
/ 1133
7.6%
t 1103
7.4%
d 938
6.3%
h 908
 
6.1%
s 868
 
5.8%
r 542
 
3.7%
Other values (15) 2149
14.5%

Aroma
Real number (ℝ)

Distinct33
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5638063
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:14.137498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.42
median7.58
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.37866631
Coefficient of variation (CV)0.050062931
Kurtosis122.3781
Mean7.5638063
Median Absolute Deviation (MAD)0.17
Skewness-6.306326
Sum9916.15
Variance0.14338817
MonotonicityNot monotonic
2023-07-24T18:57:14.309375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.67 173
13.2%
7.5 163
12.4%
7.58 149
11.4%
7.75 122
9.3%
7.42 121
9.2%
7.83 101
7.7%
7.33 96
7.3%
7.25 78
 
5.9%
7.92 57
 
4.3%
7.17 45
 
3.4%
Other values (23) 206
15.7%
ValueCountFrequency (%)
0 1
 
0.1%
5.08 1
 
0.1%
6.17 1
 
0.1%
6.33 1
 
0.1%
6.42 1
 
0.1%
6.5 2
 
0.2%
6.67 3
 
0.2%
6.75 6
0.5%
6.83 9
0.7%
6.92 14
1.1%
ValueCountFrequency (%)
8.75 1
 
0.1%
8.67 2
 
0.2%
8.58 1
 
0.1%
8.5 3
 
0.2%
8.42 9
 
0.7%
8.33 6
 
0.5%
8.25 9
 
0.7%
8.17 20
1.5%
8.08 20
1.5%
8 43
3.3%

Flavor
Real number (ℝ)

Distinct35
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5180702
Minimum0
Maximum8.83
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:14.496873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.58
Q37.75
95-th percentile8
Maximum8.83
Range8.83
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.39997921
Coefficient of variation (CV)0.053202378
Kurtosis95.172934
Mean7.5180702
Median Absolute Deviation (MAD)0.17
Skewness-5.2235119
Sum9856.19
Variance0.15998337
MonotonicityNot monotonic
2023-07-24T18:57:14.668748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5 164
12.5%
7.58 162
12.4%
7.67 145
11.1%
7.75 120
9.2%
7.42 114
8.7%
7.33 110
8.4%
7.83 85
 
6.5%
7.25 64
 
4.9%
7.17 56
 
4.3%
7.08 42
 
3.2%
Other values (25) 249
19.0%
ValueCountFrequency (%)
0 1
 
0.1%
6.08 1
 
0.1%
6.17 2
 
0.2%
6.33 3
 
0.2%
6.42 1
 
0.1%
6.5 9
0.7%
6.58 5
 
0.4%
6.67 4
 
0.3%
6.75 10
0.8%
6.83 16
1.2%
ValueCountFrequency (%)
8.83 1
 
0.1%
8.67 4
 
0.3%
8.58 2
 
0.2%
8.5 5
 
0.4%
8.42 5
 
0.4%
8.33 5
 
0.4%
8.25 7
 
0.5%
8.17 18
1.4%
8.08 13
 
1.0%
8 41
3.1%

Aftertaste
Real number (ℝ)

Distinct35
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3976964
Minimum0
Maximum8.67
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:14.840907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.42
Q37.58
95-th percentile7.92
Maximum8.67
Range8.67
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.40511863
Coefficient of variation (CV)0.054762808
Kurtosis84.644948
Mean7.3976964
Median Absolute Deviation (MAD)0.17
Skewness-4.8450553
Sum9698.38
Variance0.1641211
MonotonicityNot monotonic
2023-07-24T18:57:15.012783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5 162
12.4%
7.33 150
11.4%
7.42 127
9.7%
7.58 125
9.5%
7.25 103
7.9%
7.67 99
 
7.6%
7.17 90
 
6.9%
7.75 81
 
6.2%
7 62
 
4.7%
7.83 61
 
4.7%
Other values (25) 251
19.1%
ValueCountFrequency (%)
0 1
 
0.1%
6.17 8
 
0.6%
6.25 1
 
0.1%
6.33 6
 
0.5%
6.42 4
 
0.3%
6.5 6
 
0.5%
6.58 6
 
0.5%
6.67 14
 
1.1%
6.75 9
 
0.7%
6.83 36
2.7%
ValueCountFrequency (%)
8.67 1
 
0.1%
8.58 2
 
0.2%
8.5 4
 
0.3%
8.42 3
 
0.2%
8.33 2
 
0.2%
8.25 4
 
0.3%
8.17 7
 
0.5%
8.08 7
 
0.5%
8 27
2.1%
7.92 19
1.4%

Acidity
Real number (ℝ)

Distinct31
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5331121
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:15.200281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.38159879
Coefficient of variation (CV)0.050656194
Kurtosis116.27208
Mean7.5331121
Median Absolute Deviation (MAD)0.17
Skewness-5.9678735
Sum9875.91
Variance0.14561764
MonotonicityNot monotonic
2023-07-24T18:57:15.356529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5 160
12.2%
7.58 150
11.4%
7.67 143
10.9%
7.42 127
9.7%
7.75 122
9.3%
7.33 110
8.4%
7.25 86
 
6.6%
7.17 73
 
5.6%
7.83 72
 
5.5%
8 47
 
3.6%
Other values (21) 221
16.9%
ValueCountFrequency (%)
0 1
 
0.1%
5.25 1
 
0.1%
6.08 1
 
0.1%
6.25 1
 
0.1%
6.5 1
 
0.1%
6.67 5
 
0.4%
6.75 6
 
0.5%
6.83 11
 
0.8%
6.92 10
 
0.8%
7 32
2.4%
ValueCountFrequency (%)
8.75 1
 
0.1%
8.58 1
 
0.1%
8.5 7
 
0.5%
8.42 6
 
0.5%
8.33 9
 
0.7%
8.25 6
 
0.5%
8.17 14
 
1.1%
8.08 25
1.9%
8 47
3.6%
7.92 46
3.5%

Body
Real number (ℝ)

Distinct31
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5177269
Minimum0
Maximum8.58
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:15.528404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.33
median7.5
Q37.67
95-th percentile8
Maximum8.58
Range8.58
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.35921291
Coefficient of variation (CV)0.047782117
Kurtosis146.90949
Mean7.5177269
Median Absolute Deviation (MAD)0.17
Skewness-7.1554372
Sum9855.74
Variance0.12903391
MonotonicityNot monotonic
2023-07-24T18:57:15.700280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5 198
15.1%
7.67 149
11.4%
7.58 136
10.4%
7.33 131
10.0%
7.42 125
9.5%
7.75 108
8.2%
7.25 86
6.6%
7.83 82
6.3%
7.17 68
 
5.2%
7.92 48
 
3.7%
Other values (21) 180
13.7%
ValueCountFrequency (%)
0 1
 
0.1%
5.25 1
 
0.1%
6.33 2
 
0.2%
6.42 1
 
0.1%
6.5 1
 
0.1%
6.67 2
 
0.2%
6.75 4
 
0.3%
6.83 4
 
0.3%
6.92 11
 
0.8%
7 34
2.6%
ValueCountFrequency (%)
8.58 1
 
0.1%
8.5 3
 
0.2%
8.42 3
 
0.2%
8.33 6
 
0.5%
8.25 5
 
0.4%
8.17 7
 
0.5%
8.08 21
 
1.6%
8 34
2.6%
7.92 48
3.7%
7.83 82
6.3%

Balance
Real number (ℝ)

Distinct32
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5175057
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:15.872159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.40631592
Coefficient of variation (CV)0.0540493
Kurtosis89.118262
Mean7.5175057
Median Absolute Deviation (MAD)0.17
Skewness-4.8442801
Sum9855.45
Variance0.16509263
MonotonicityNot monotonic
2023-07-24T18:57:16.044031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
7.5 172
13.1%
7.67 145
11.1%
7.58 127
9.7%
7.42 120
9.2%
7.75 103
 
7.9%
7.33 99
 
7.6%
7.83 98
 
7.5%
7.17 71
 
5.4%
7.25 64
 
4.9%
7 46
 
3.5%
Other values (22) 266
20.3%
ValueCountFrequency (%)
0 1
 
0.1%
6.08 1
 
0.1%
6.17 3
 
0.2%
6.33 1
 
0.1%
6.42 1
 
0.1%
6.5 2
 
0.2%
6.58 3
 
0.2%
6.67 4
 
0.3%
6.75 7
 
0.5%
6.83 22
1.7%
ValueCountFrequency (%)
8.75 2
 
0.2%
8.58 7
 
0.5%
8.5 7
 
0.5%
8.42 7
 
0.5%
8.33 7
 
0.5%
8.25 8
 
0.6%
8.17 17
 
1.3%
8.08 16
 
1.2%
8 45
3.4%
7.92 38
2.9%

Uniformity
Real number (ℝ)

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8333944
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:16.200280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.67
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55934312
Coefficient of variation (CV)0.056881998
Kurtosis84.152305
Mean9.8333944
Median Absolute Deviation (MAD)0
Skewness-6.9261173
Sum12891.58
Variance0.31286473
MonotonicityNot monotonic
2023-07-24T18:57:16.356530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 1128
86.0%
9.33 112
 
8.5%
8.67 31
 
2.4%
8 25
 
1.9%
6.67 7
 
0.5%
6 3
 
0.2%
7.33 2
 
0.2%
9.5 1
 
0.1%
9 1
 
0.1%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
6 3
 
0.2%
6.67 7
 
0.5%
7.33 2
 
0.2%
8 25
 
1.9%
8.67 31
 
2.4%
9 1
 
0.1%
9.33 112
 
8.5%
9.5 1
 
0.1%
10 1128
86.0%
ValueCountFrequency (%)
10 1128
86.0%
9.5 1
 
0.1%
9.33 112
 
8.5%
9 1
 
0.1%
8.67 31
 
2.4%
8 25
 
1.9%
7.33 2
 
0.2%
6.67 7
 
0.5%
6 3
 
0.2%
0 1
 
0.1%

Clean.Cup
Real number (ℝ)

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8331198
Minimum0
Maximum10
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:16.512775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77134973
Coefficient of variation (CV)0.07844405
Kurtosis69.173176
Mean9.8331198
Median Absolute Deviation (MAD)0
Skewness-7.3778242
Sum12891.22
Variance0.59498041
MonotonicityNot monotonic
2023-07-24T18:57:16.653404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 1194
91.1%
9.33 58
 
4.4%
8.67 16
 
1.2%
6.67 13
 
1.0%
8 13
 
1.0%
6 6
 
0.5%
5.33 3
 
0.2%
7.33 3
 
0.2%
2.67 2
 
0.2%
0 2
 
0.2%
ValueCountFrequency (%)
0 2
 
0.2%
1.33 1
 
0.1%
2.67 2
 
0.2%
5.33 3
 
0.2%
6 6
 
0.5%
6.67 13
 
1.0%
7.33 3
 
0.2%
8 13
 
1.0%
8.67 16
 
1.2%
9.33 58
4.4%
ValueCountFrequency (%)
10 1194
91.1%
9.33 58
 
4.4%
8.67 16
 
1.2%
8 13
 
1.0%
7.33 3
 
0.2%
6.67 13
 
1.0%
6 6
 
0.5%
5.33 3
 
0.2%
2.67 2
 
0.2%
1.33 1
 
0.1%

Sweetness
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9032723
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:16.809653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.53083174
Coefficient of variation (CV)0.05360165
Kurtosis157.52829
Mean9.9032723
Median Absolute Deviation (MAD)0
Skewness-10.756332
Sum12983.19
Variance0.28178234
MonotonicityNot monotonic
2023-07-24T18:57:16.950281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 1218
92.9%
9.33 61
 
4.7%
8.67 12
 
0.9%
8 8
 
0.6%
6.67 7
 
0.5%
6 3
 
0.2%
1.33 1
 
0.1%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
1.33 1
 
0.1%
6 3
 
0.2%
6.67 7
 
0.5%
8 8
 
0.6%
8.67 12
 
0.9%
9.33 61
 
4.7%
10 1218
92.9%
ValueCountFrequency (%)
10 1218
92.9%
9.33 61
 
4.7%
8.67 12
 
0.9%
8 8
 
0.6%
6.67 7
 
0.5%
6 3
 
0.2%
1.33 1
 
0.1%
0 1
 
0.1%

Cupper.Points
Real number (ℝ)

Distinct42
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4978642
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:17.137905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.5
Q37.75
95-th percentile8.08
Maximum10
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.47461002
Coefficient of variation (CV)0.063299362
Kurtosis50.156431
Mean7.4978642
Median Absolute Deviation (MAD)0.25
Skewness-2.8388745
Sum9829.7
Variance0.22525467
MonotonicityNot monotonic
2023-07-24T18:57:17.325402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7.5 152
11.6%
7.58 136
10.4%
7.33 114
 
8.7%
7.67 113
 
8.6%
7.42 103
 
7.9%
7.25 85
 
6.5%
7.75 84
 
6.4%
7.83 81
 
6.2%
7.17 63
 
4.8%
7.92 52
 
4.0%
Other values (32) 328
25.0%
ValueCountFrequency (%)
0 1
 
0.1%
5.17 1
 
0.1%
5.25 1
 
0.1%
5.42 1
 
0.1%
6 1
 
0.1%
6.17 3
0.2%
6.25 1
 
0.1%
6.33 3
0.2%
6.42 5
0.4%
6.5 6
0.5%
ValueCountFrequency (%)
10 4
0.3%
9.25 1
 
0.1%
9 1
 
0.1%
8.83 1
 
0.1%
8.75 1
 
0.1%
8.67 2
 
0.2%
8.58 5
0.4%
8.5 8
0.6%
8.42 6
0.5%
8.33 8
0.6%

Total.Cup.Points
Real number (ℝ)

Distinct178
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.115927
Minimum0
Maximum90.58
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:17.544159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.92
Q181.17
median82.5
Q383.67
95-th percentile85.5
Maximum90.58
Range90.58
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.5157607
Coefficient of variation (CV)0.042814601
Kurtosis229.25664
Mean82.115927
Median Absolute Deviation (MAD)1.25
Skewness-10.529617
Sum107653.98
Variance12.360573
MonotonicityDecreasing
2023-07-24T18:57:17.731658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.17 38
 
2.9%
83 37
 
2.8%
82.42 31
 
2.4%
82.75 29
 
2.2%
82.33 29
 
2.2%
82.67 26
 
2.0%
81.83 26
 
2.0%
82.92 26
 
2.0%
81.67 25
 
1.9%
81.5 24
 
1.8%
Other values (168) 1020
77.8%
ValueCountFrequency (%)
0 1
0.1%
59.83 1
0.1%
63.08 1
0.1%
67.92 1
0.1%
68.33 1
0.1%
69.17 2
0.2%
69.33 1
0.1%
70.67 1
0.1%
70.75 1
0.1%
71 1
0.1%
ValueCountFrequency (%)
90.58 1
0.1%
89.92 1
0.1%
89.75 1
0.1%
89 1
0.1%
88.83 2
0.2%
88.75 1
0.1%
88.67 1
0.1%
88.42 1
0.1%
88.25 1
0.1%
88.08 1
0.1%

Moisture
Real number (ℝ)

Distinct23
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.088863463
Minimum0
Maximum0.28
Zeros252
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:17.903531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.11
Q30.12
95-th percentile0.13
Maximum0.28
Range0.28
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.047956776
Coefficient of variation (CV)0.53966809
Kurtosis-0.096267744
Mean0.088863463
Median Absolute Deviation (MAD)0.01
Skewness-1.0109972
Sum116.5
Variance0.0022998523
MonotonicityNot monotonic
2023-07-24T18:57:18.059785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.11 381
29.1%
0.12 284
21.7%
0 252
19.2%
0.1 180
13.7%
0.13 75
 
5.7%
0.09 26
 
2.0%
0.14 23
 
1.8%
0.08 16
 
1.2%
0.01 15
 
1.1%
0.15 8
 
0.6%
Other values (13) 51
 
3.9%
ValueCountFrequency (%)
0 252
19.2%
0.01 15
 
1.1%
0.02 7
 
0.5%
0.03 4
 
0.3%
0.04 4
 
0.3%
0.05 8
 
0.6%
0.06 7
 
0.5%
0.07 5
 
0.4%
0.08 16
 
1.2%
0.09 26
 
2.0%
ValueCountFrequency (%)
0.28 1
 
0.1%
0.22 1
 
0.1%
0.21 1
 
0.1%
0.2 3
 
0.2%
0.18 2
 
0.2%
0.17 3
 
0.2%
0.16 5
 
0.4%
0.15 8
 
0.6%
0.14 23
 
1.8%
0.13 75
5.7%

Category.One.Defects
Real number (ℝ)

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42639207
Minimum0
Maximum31
Zeros1111
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:18.232114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8324153
Coefficient of variation (CV)4.2974891
Kurtosis142.6272
Mean0.42639207
Median Absolute Deviation (MAD)0
Skewness10.240339
Sum559
Variance3.3577457
MonotonicityNot monotonic
2023-07-24T18:57:18.388361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1111
84.7%
1 101
 
7.7%
2 38
 
2.9%
3 18
 
1.4%
4 16
 
1.2%
5 9
 
0.7%
10 4
 
0.3%
6 3
 
0.2%
7 3
 
0.2%
31 2
 
0.2%
Other values (6) 6
 
0.5%
ValueCountFrequency (%)
0 1111
84.7%
1 101
 
7.7%
2 38
 
2.9%
3 18
 
1.4%
4 16
 
1.2%
5 9
 
0.7%
6 3
 
0.2%
7 3
 
0.2%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
31 2
0.2%
23 1
 
0.1%
15 1
 
0.1%
12 1
 
0.1%
11 1
 
0.1%
10 4
0.3%
9 1
 
0.1%
8 1
 
0.1%
7 3
0.2%
6 3
0.2%

Quakers
Real number (ℝ)

Distinct11
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.17709924
Minimum0
Maximum11
Zeros1216
Zeros (%)92.8%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:18.544609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84058302
Coefficient of variation (CV)4.7463955
Kurtosis57.045449
Mean0.17709924
Median Absolute Deviation (MAD)0
Skewness6.860057
Sum232
Variance0.70657981
MonotonicityNot monotonic
2023-07-24T18:57:18.685237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1216
92.8%
1 39
 
3.0%
2 30
 
2.3%
4 5
 
0.4%
5 5
 
0.4%
3 5
 
0.4%
6 4
 
0.3%
7 3
 
0.2%
11 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
0 1216
92.8%
1 39
 
3.0%
2 30
 
2.3%
3 5
 
0.4%
4 5
 
0.4%
5 5
 
0.4%
6 4
 
0.3%
7 3
 
0.2%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
9 1
 
0.1%
8 1
 
0.1%
7 3
 
0.2%
6 4
 
0.3%
5 5
 
0.4%
4 5
 
0.4%
3 5
 
0.4%
2 30
2.3%
1 39
3.0%

Color
Categorical

Distinct4
Distinct (%)0.4%
Missing216
Missing (%)16.5%
Memory size20.5 KiB
Green
850 
Bluish-Green
112 
Blue-Green
 
82
None
 
51

Length

Max length12
Median length5
Mean length6.0438356
Min length4

Characters and Unicode

Total characters6618
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreen
2nd rowGreen
3rd rowGreen
4th rowGreen
5th rowBluish-Green

Common Values

ValueCountFrequency (%)
Green 850
64.8%
Bluish-Green 112
 
8.5%
Blue-Green 82
 
6.3%
None 51
 
3.9%
(Missing) 216
 
16.5%

Length

2023-07-24T18:57:18.857915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T18:57:19.055659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
green 850
77.6%
bluish-green 112
 
10.2%
blue-green 82
 
7.5%
none 51
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e 2221
33.6%
n 1095
16.5%
G 1044
15.8%
r 1044
15.8%
B 194
 
2.9%
l 194
 
2.9%
u 194
 
2.9%
- 194
 
2.9%
i 112
 
1.7%
s 112
 
1.7%
Other values (3) 214
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5135
77.6%
Uppercase Letter 1289
 
19.5%
Dash Punctuation 194
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2221
43.3%
n 1095
21.3%
r 1044
20.3%
l 194
 
3.8%
u 194
 
3.8%
i 112
 
2.2%
s 112
 
2.2%
h 112
 
2.2%
o 51
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
G 1044
81.0%
B 194
 
15.1%
N 51
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6424
97.1%
Common 194
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2221
34.6%
n 1095
17.0%
G 1044
16.3%
r 1044
16.3%
B 194
 
3.0%
l 194
 
3.0%
u 194
 
3.0%
i 112
 
1.7%
s 112
 
1.7%
h 112
 
1.7%
Other values (2) 102
 
1.6%
Common
ValueCountFrequency (%)
- 194
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2221
33.6%
n 1095
16.5%
G 1044
15.8%
r 1044
15.8%
B 194
 
2.9%
l 194
 
2.9%
u 194
 
2.9%
- 194
 
2.9%
i 112
 
1.7%
s 112
 
1.7%
Other values (3) 214
 
3.2%

Category.Two.Defects
Real number (ℝ)

Distinct38
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5919146
Minimum0
Maximum55
Zeros362
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:19.227772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile13
Maximum55
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.3503709
Coefficient of variation (CV)1.4895596
Kurtosis19.817738
Mean3.5919146
Median Absolute Deviation (MAD)2
Skewness3.6485653
Sum4709
Variance28.626469
MonotonicityNot monotonic
2023-07-24T18:57:19.399646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 362
27.6%
1 200
15.3%
2 178
13.6%
3 133
 
10.1%
4 118
 
9.0%
5 73
 
5.6%
6 42
 
3.2%
7 39
 
3.0%
8 29
 
2.2%
9 22
 
1.7%
Other values (28) 115
 
8.8%
ValueCountFrequency (%)
0 362
27.6%
1 200
15.3%
2 178
13.6%
3 133
 
10.1%
4 118
 
9.0%
5 73
 
5.6%
6 42
 
3.2%
7 39
 
3.0%
8 29
 
2.2%
9 22
 
1.7%
ValueCountFrequency (%)
55 1
0.1%
47 1
0.1%
45 1
0.1%
40 1
0.1%
38 1
0.1%
34 1
0.1%
32 1
0.1%
31 1
0.1%
30 2
0.2%
29 2
0.2%

Expiration
Categorical

Distinct557
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
July 11th, 2013
 
25
December 26th, 2014
 
25
June 6th, 2013
 
19
August 30th, 2013
 
18
July 26th, 2013
 
15
Other values (552)
1209 

Length

Max length20
Median length18
Mean length16.603356
Min length13

Characters and Unicode

Total characters21767
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)21.1%

Sample

1st rowApril 3rd, 2016
2nd rowApril 3rd, 2016
3rd rowMay 31st, 2011
4th rowMarch 25th, 2016
5th rowApril 3rd, 2016

Common Values

ValueCountFrequency (%)
July 11th, 2013 25
 
1.9%
December 26th, 2014 25
 
1.9%
June 6th, 2013 19
 
1.4%
August 30th, 2013 18
 
1.4%
July 26th, 2013 15
 
1.1%
October 7th, 2016 13
 
1.0%
September 27th, 2013 13
 
1.0%
March 29th, 2014 13
 
1.0%
June 17th, 2011 12
 
0.9%
September 17th, 2013 11
 
0.8%
Other values (547) 1147
87.5%

Length

2023-07-24T18:57:19.587551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013 314
 
8.0%
2015 246
 
6.3%
2016 195
 
5.0%
june 154
 
3.9%
2014 153
 
3.9%
2018 141
 
3.6%
july 127
 
3.2%
april 127
 
3.2%
may 125
 
3.2%
2017 123
 
3.1%
Other values (42) 2228
56.6%

Most occurring characters

ValueCountFrequency (%)
2622
 
12.0%
2 1987
 
9.1%
1 1940
 
8.9%
0 1437
 
6.6%
t 1429
 
6.6%
, 1311
 
6.0%
h 1156
 
5.3%
e 1068
 
4.9%
r 972
 
4.5%
u 712
 
3.3%
Other values (30) 7133
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9028
41.5%
Decimal Number 7494
34.4%
Space Separator 2622
 
12.0%
Other Punctuation 1311
 
6.0%
Uppercase Letter 1311
 
6.0%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1429
15.8%
h 1156
12.8%
e 1068
11.8%
r 972
10.8%
u 712
7.9%
a 550
 
6.1%
y 459
 
5.1%
b 448
 
5.0%
n 363
 
4.0%
c 283
 
3.1%
Other values (9) 1588
17.6%
Decimal Number
ValueCountFrequency (%)
2 1987
26.5%
1 1940
25.9%
0 1437
19.2%
3 498
 
6.6%
6 393
 
5.2%
5 358
 
4.8%
7 276
 
3.7%
4 245
 
3.3%
8 241
 
3.2%
9 119
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
J 383
29.2%
M 241
18.4%
A 239
18.2%
S 110
 
8.4%
F 105
 
8.0%
D 90
 
6.9%
O 77
 
5.9%
N 66
 
5.0%
Space Separator
ValueCountFrequency (%)
2622
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1311
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11428
52.5%
Latin 10339
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1429
13.8%
h 1156
11.2%
e 1068
 
10.3%
r 972
 
9.4%
u 712
 
6.9%
a 550
 
5.3%
y 459
 
4.4%
b 448
 
4.3%
J 383
 
3.7%
n 363
 
3.5%
Other values (17) 2799
27.1%
Common
ValueCountFrequency (%)
2622
22.9%
2 1987
17.4%
1 1940
17.0%
0 1437
12.6%
, 1311
11.5%
3 498
 
4.4%
6 393
 
3.4%
5 358
 
3.1%
7 276
 
2.4%
4 245
 
2.1%
Other values (3) 361
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2622
 
12.0%
2 1987
 
9.1%
1 1940
 
8.9%
0 1437
 
6.6%
t 1429
 
6.6%
, 1311
 
6.0%
h 1156
 
5.3%
e 1068
 
4.9%
r 972
 
4.5%
u 712
 
3.3%
Other values (30) 7133
32.8%

Certification.Body
Categorical

Distinct26
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
Specialty Coffee Association
295 
AMECAFE
205 
Almacafé
178 
Asociacion Nacional Del Café
155 
Brazil Specialty Coffee Association
67 
Other values (21)
411 

Length

Max length85
Median length62
Mean length22.946606
Min length7

Characters and Unicode

Total characters30083
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowMETAD Agricultural Development plc
2nd rowMETAD Agricultural Development plc
3rd rowSpecialty Coffee Association
4th rowMETAD Agricultural Development plc
5th rowMETAD Agricultural Development plc

Common Values

ValueCountFrequency (%)
Specialty Coffee Association 295
22.5%
AMECAFE 205
15.6%
Almacafé 178
13.6%
Asociacion Nacional Del Café 155
11.8%
Brazil Specialty Coffee Association 67
 
5.1%
Instituto Hondureño del Café 60
 
4.6%
Blossom Valley International 58
 
4.4%
Africa Fine Coffee Association 49
 
3.7%
Specialty Coffee Association of Costa Rica 43
 
3.3%
NUCOFFEE 36
 
2.7%
Other values (16) 165
12.6%

Length

2023-07-24T18:57:19.744079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coffee 562
15.0%
association 486
12.9%
specialty 431
11.5%
café 225
 
6.0%
del 224
 
6.0%
amecafe 205
 
5.5%
almacafé 178
 
4.7%
asociacion 155
 
4.1%
nacional 155
 
4.1%
of 69
 
1.8%
Other values (51) 1066
28.4%

Most occurring characters

ValueCountFrequency (%)
a 2704
 
9.0%
o 2672
 
8.9%
2445
 
8.1%
i 2431
 
8.1%
e 2314
 
7.7%
c 1801
 
6.0%
f 1665
 
5.5%
t 1483
 
4.9%
s 1477
 
4.9%
l 1426
 
4.7%
Other values (43) 9665
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22472
74.7%
Uppercase Letter 5135
 
17.1%
Space Separator 2445
 
8.1%
Other Punctuation 29
 
0.1%
Dash Punctuation 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2704
12.0%
o 2672
11.9%
i 2431
10.8%
e 2314
10.3%
c 1801
8.0%
f 1665
7.4%
t 1483
6.6%
s 1477
6.6%
l 1426
6.3%
n 1410
6.3%
Other values (17) 3089
13.7%
Uppercase Letter
ValueCountFrequency (%)
A 1382
26.9%
C 1146
22.3%
E 559
10.9%
S 442
 
8.6%
F 326
 
6.3%
M 226
 
4.4%
D 222
 
4.3%
N 199
 
3.9%
I 152
 
3.0%
B 132
 
2.6%
Other values (11) 349
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 28
96.6%
& 1
 
3.4%
Space Separator
ValueCountFrequency (%)
2445
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27607
91.8%
Common 2476
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2704
 
9.8%
o 2672
 
9.7%
i 2431
 
8.8%
e 2314
 
8.4%
c 1801
 
6.5%
f 1665
 
6.0%
t 1483
 
5.4%
s 1477
 
5.4%
l 1426
 
5.2%
n 1410
 
5.1%
Other values (38) 8224
29.8%
Common
ValueCountFrequency (%)
2445
98.7%
. 28
 
1.1%
- 1
 
< 0.1%
& 1
 
< 0.1%
1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29577
98.3%
None 506
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2704
 
9.1%
o 2672
 
9.0%
2445
 
8.3%
i 2431
 
8.2%
e 2314
 
7.8%
c 1801
 
6.1%
f 1665
 
5.6%
t 1483
 
5.0%
s 1477
 
5.0%
l 1426
 
4.8%
Other values (38) 9159
31.0%
None
ValueCountFrequency (%)
é 417
82.4%
ñ 60
 
11.9%
ó 22
 
4.3%
í 6
 
1.2%
ú 1
 
0.2%

Certification.Address
Categorical

Distinct30
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
36d0d00a3724338ba7937c52a378d085f2172daa
293 
59e396ad6e22a1c22b248f958e1da2bd8af85272
204 
e493c36c2d076bf273064f7ac23ad562af257a25
178 
b1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53
155 
3297cfa4c538e3dd03f72cc4082c54f7999e1f9d
67 
Other values (25)
414 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters52440
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.4%

Sample

1st row309fcf77415a3661ae83e027f7e5f05dad786e44
2nd row309fcf77415a3661ae83e027f7e5f05dad786e44
3rd row36d0d00a3724338ba7937c52a378d085f2172daa
4th row309fcf77415a3661ae83e027f7e5f05dad786e44
5th row309fcf77415a3661ae83e027f7e5f05dad786e44

Common Values

ValueCountFrequency (%)
36d0d00a3724338ba7937c52a378d085f2172daa 293
22.3%
59e396ad6e22a1c22b248f958e1da2bd8af85272 204
15.6%
e493c36c2d076bf273064f7ac23ad562af257a25 178
13.6%
b1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53 155
11.8%
3297cfa4c538e3dd03f72cc4082c54f7999e1f9d 67
 
5.1%
b4660a57e9f8cc613ae5b8f02bfce8634c763ab4 60
 
4.6%
fc45352eee499d8470cf94c9827922fb745bf815 58
 
4.4%
073285c0d45e2f5539012d969937e529564fa6fe 48
 
3.7%
8e0b118f3cf3121ab27c5387deacdb7d4d2a60b1 42
 
3.2%
567f200bcc17a90070cb952647bf88141ad9c80c 36
 
2.7%
Other values (20) 170
13.0%

Length

2023-07-24T18:57:19.916631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36d0d00a3724338ba7937c52a378d085f2172daa 293
22.3%
59e396ad6e22a1c22b248f958e1da2bd8af85272 204
15.6%
e493c36c2d076bf273064f7ac23ad562af257a25 178
13.6%
b1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53 155
11.8%
3297cfa4c538e3dd03f72cc4082c54f7999e1f9d 67
 
5.1%
b4660a57e9f8cc613ae5b8f02bfce8634c763ab4 60
 
4.6%
fc45352eee499d8470cf94c9827922fb745bf815 58
 
4.4%
073285c0d45e2f5539012d969937e529564fa6fe 48
 
3.7%
8e0b118f3cf3121ab27c5387deacdb7d4d2a60b1 42
 
3.2%
567f200bcc17a90070cb952647bf88141ad9c80c 36
 
2.7%
Other values (20) 170
13.0%

Most occurring characters

ValueCountFrequency (%)
2 5585
 
10.7%
3 4308
 
8.2%
a 4073
 
7.8%
7 3677
 
7.0%
d 3615
 
6.9%
f 3412
 
6.5%
8 3306
 
6.3%
5 3230
 
6.2%
0 3128
 
6.0%
6 2889
 
5.5%
Other values (6) 15217
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33456
63.8%
Lowercase Letter 18984
36.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5585
16.7%
3 4308
12.9%
7 3677
11.0%
8 3306
9.9%
5 3230
9.7%
0 3128
9.3%
6 2889
8.6%
9 2684
8.0%
4 2537
7.6%
1 2112
 
6.3%
Lowercase Letter
ValueCountFrequency (%)
a 4073
21.5%
d 3615
19.0%
f 3412
18.0%
e 2755
14.5%
c 2678
14.1%
b 2451
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 33456
63.8%
Latin 18984
36.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5585
16.7%
3 4308
12.9%
7 3677
11.0%
8 3306
9.9%
5 3230
9.7%
0 3128
9.3%
6 2889
8.6%
9 2684
8.0%
4 2537
7.6%
1 2112
 
6.3%
Latin
ValueCountFrequency (%)
a 4073
21.5%
d 3615
19.0%
f 3412
18.0%
e 2755
14.5%
c 2678
14.1%
b 2451
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5585
 
10.7%
3 4308
 
8.2%
a 4073
 
7.8%
7 3677
 
7.0%
d 3615
 
6.9%
f 3412
 
6.5%
8 3306
 
6.3%
5 3230
 
6.2%
0 3128
 
6.0%
6 2889
 
5.5%
Other values (6) 15217
29.0%

Certification.Contact
Categorical

Distinct27
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660
295 
0eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7
204 
70d3c0c26f89e00fdae6fb39ff54f0d2eb1c38ab
178 
724f04ad10ed31dbb9d260f0dfd221ba48be8a95
155 
8900f0bf1d0b2bafe6807a73562c7677d57eb980
67 
Other values (22)
412 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters52440
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row19fef5a731de2db57d16da10287413f5f99bc2dd
2nd row19fef5a731de2db57d16da10287413f5f99bc2dd
3rd row0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660
4th row19fef5a731de2db57d16da10287413f5f99bc2dd
5th row19fef5a731de2db57d16da10287413f5f99bc2dd

Common Values

ValueCountFrequency (%)
0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660 295
22.5%
0eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7 204
15.6%
70d3c0c26f89e00fdae6fb39ff54f0d2eb1c38ab 178
13.6%
724f04ad10ed31dbb9d260f0dfd221ba48be8a95 155
11.8%
8900f0bf1d0b2bafe6807a73562c7677d57eb980 67
 
5.1%
7f521ca403540f81ec99daec7da19c2788393880 60
 
4.6%
de73fc9412358b523d3a641501e542f31d2668b0 58
 
4.4%
c4ab13415cdd69376a93780c0166e7b1a10481ea 49
 
3.7%
5eb2b7129d9714c43825e44dc3bca9423de209e9 43
 
3.3%
aa2ff513ffb9c844462a1fb07c599bce7f3bb53d 36
 
2.7%
Other values (17) 166
12.7%

Length

2023-07-24T18:57:20.090279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660 295
22.5%
0eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7 204
15.6%
70d3c0c26f89e00fdae6fb39ff54f0d2eb1c38ab 178
13.6%
724f04ad10ed31dbb9d260f0dfd221ba48be8a95 155
11.8%
8900f0bf1d0b2bafe6807a73562c7677d57eb980 67
 
5.1%
7f521ca403540f81ec99daec7da19c2788393880 60
 
4.6%
de73fc9412358b523d3a641501e542f31d2668b0 58
 
4.4%
c4ab13415cdd69376a93780c0166e7b1a10481ea 49
 
3.7%
5eb2b7129d9714c43825e44dc3bca9423de209e9 43
 
3.3%
aa2ff513ffb9c844462a1fb07c599bce7f3bb53d 36
 
2.7%
Other values (17) 166
12.7%

Most occurring characters

ValueCountFrequency (%)
0 5015
 
9.6%
d 4337
 
8.3%
b 4219
 
8.0%
2 3761
 
7.2%
f 3585
 
6.8%
e 3585
 
6.8%
a 3504
 
6.7%
4 3371
 
6.4%
7 3176
 
6.1%
6 2788
 
5.3%
Other values (6) 15099
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30631
58.4%
Lowercase Letter 21809
41.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5015
16.4%
2 3761
12.3%
4 3371
11.0%
7 3176
10.4%
6 2788
9.1%
9 2709
8.8%
8 2640
8.6%
3 2563
8.4%
5 2386
7.8%
1 2222
7.3%
Lowercase Letter
ValueCountFrequency (%)
d 4337
19.9%
b 4219
19.3%
f 3585
16.4%
e 3585
16.4%
a 3504
16.1%
c 2579
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 30631
58.4%
Latin 21809
41.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5015
16.4%
2 3761
12.3%
4 3371
11.0%
7 3176
10.4%
6 2788
9.1%
9 2709
8.8%
8 2640
8.6%
3 2563
8.4%
5 2386
7.8%
1 2222
7.3%
Latin
ValueCountFrequency (%)
d 4337
19.9%
b 4219
19.3%
f 3585
16.4%
e 3585
16.4%
a 3504
16.1%
c 2579
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5015
 
9.6%
d 4337
 
8.3%
b 4219
 
8.0%
2 3761
 
7.2%
f 3585
 
6.8%
e 3585
 
6.8%
a 3504
 
6.7%
4 3371
 
6.4%
7 3176
 
6.1%
6 2788
 
5.3%
Other values (6) 15099
28.8%

unit_of_measurement
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.5 KiB
m
1129 
ft
182 

Length

Max length2
Median length1
Mean length1.1388253
Min length1

Characters and Unicode

Total characters1493
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm
2nd rowm
3rd rowm
4th rowm
5th rowm

Common Values

ValueCountFrequency (%)
m 1129
86.1%
ft 182
 
13.9%

Length

2023-07-24T18:57:20.246530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T18:57:20.402777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
m 1129
86.1%
ft 182
 
13.9%

Most occurring characters

ValueCountFrequency (%)
m 1129
75.6%
f 182
 
12.2%
t 182
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1493
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1129
75.6%
f 182
 
12.2%
t 182
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1493
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1129
75.6%
f 182
 
12.2%
t 182
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1129
75.6%
f 182
 
12.2%
t 182
 
12.2%

altitude_low_meters
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct188
Distinct (%)17.3%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1759.549
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:20.574654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1850
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8767.8473
Coefficient of variation (CV)4.9830084
Kurtosis414.47218
Mean1759.549
Median Absolute Deviation (MAD)239.36
Skewness20.097874
Sum1907351.1
Variance76875145
MonotonicityNot monotonic
2023-07-24T18:57:20.777775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 80
 
6.1%
1600 65
 
5.0%
1400 59
 
4.5%
1100 55
 
4.2%
1500 54
 
4.1%
1300 48
 
3.7%
1800 41
 
3.1%
1250 38
 
2.9%
1700 36
 
2.7%
1550 31
 
2.4%
Other values (178) 577
44.0%
(Missing) 227
 
17.3%
ValueCountFrequency (%)
1 14
1.1%
12 3
 
0.2%
13 2
 
0.2%
50 1
 
0.1%
100 2
 
0.2%
110 1
 
0.1%
125 1
 
0.1%
150 2
 
0.2%
157.8864 3
 
0.2%
160 1
 
0.1%
ValueCountFrequency (%)
190164 2
0.2%
110000 1
 
0.1%
11000 1
 
0.1%
4287 1
 
0.1%
4001 1
 
0.1%
3845 1
 
0.1%
3825 1
 
0.1%
3800 1
 
0.1%
3500 1
 
0.1%
3280 3
0.2%

altitude_high_meters
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct188
Distinct (%)17.3%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1808.8438
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:20.996525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1350
Q31650
95-th percentile1950
Maximum190164
Range190163
Interquartile range (IQR)550

Descriptive statistics

Standard deviation8767.1875
Coefficient of variation (CV)4.8468461
Kurtosis414.1443
Mean1808.8438
Median Absolute Deviation (MAD)250
Skewness20.085657
Sum1960786.7
Variance76863577
MonotonicityNot monotonic
2023-07-24T18:57:21.199656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 65
 
5.0%
1400 63
 
4.8%
1100 54
 
4.1%
1500 51
 
3.9%
1300 44
 
3.4%
1800 44
 
3.4%
1700 42
 
3.2%
1250 39
 
3.0%
1600 34
 
2.6%
1950 33
 
2.5%
Other values (178) 615
46.9%
(Missing) 227
 
17.3%
ValueCountFrequency (%)
1 12
0.9%
12 3
 
0.2%
13 2
 
0.2%
50 1
 
0.1%
100 1
 
0.1%
110 1
 
0.1%
125 1
 
0.1%
150 2
 
0.2%
157.8864 3
 
0.2%
165 1
 
0.1%
ValueCountFrequency (%)
190164 2
0.2%
110000 1
0.1%
11000 1
0.1%
5900 1
0.1%
4287 1
0.1%
4001 1
0.1%
3845 1
0.1%
3825 1
0.1%
3800 1
0.1%
3500 1
0.1%

altitude_mean_meters
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct201
Distinct (%)18.5%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1784.1964
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-24T18:57:21.418408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1880
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8767.0169
Coefficient of variation (CV)4.9137063
Kurtosis414.4035
Mean1784.1964
Median Absolute Deviation (MAD)239.36
Skewness20.095181
Sum1934068.9
Variance76860586
MonotonicityNot monotonic
2023-07-24T18:57:21.637157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 66
 
5.0%
1400 52
 
4.0%
1100 52
 
4.0%
1300 50
 
3.8%
1500 44
 
3.4%
1250 39
 
3.0%
1700 36
 
2.7%
1600 35
 
2.7%
1750 34
 
2.6%
1550 34
 
2.6%
Other values (191) 642
49.0%
(Missing) 227
 
17.3%
ValueCountFrequency (%)
1 12
0.9%
12 3
 
0.2%
13 2
 
0.2%
50 1
 
0.1%
100 1
 
0.1%
110 1
 
0.1%
125 1
 
0.1%
150 2
 
0.2%
157.8864 3
 
0.2%
165 1
 
0.1%
ValueCountFrequency (%)
190164 2
0.2%
110000 1
0.1%
11000 1
0.1%
4287 1
0.1%
4001 1
0.1%
3850 1
0.1%
3845 1
0.1%
3825 1
0.1%
3800 1
0.1%
3500 1
0.1%

Interactions

2023-07-24T18:57:03.571181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:07.472616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:10.537037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:13.552445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:16.499184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:19.712753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:22.693733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:25.791091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:28.774211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:31.787209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.017041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.007752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:41.076226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:44.323519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:47.573779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:50.733695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:53.721292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.007341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:00.293350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:03.757662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:07.645804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:10.677697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:13.697682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:16.664390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:19.857160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:22.838518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:25.945104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:28.935016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:32.154138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.182451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.178981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:41.239969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:44.476105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:47.738222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:50.897393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:53.884002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.180482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:00.477669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:03.919803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:07.802438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:10.815562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:13.862396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:17.024686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:20.013179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:22.977458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:26.085360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:29.087487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:32.317235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.334355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.330025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:41.395862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:44.598475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:47.901883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:51.026848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:54.042068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.342765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:00.639536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:04.088674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:07.956359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:10.945358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:14.017211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:17.220972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:20.156320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:23.125057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:26.235668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:29.238640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:32.469663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.481447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.491688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:41.540222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:44.769179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:48.070364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:51.180468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:54.201356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.484496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:00.812958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:04.254242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:08.102117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:11.100263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:14.140786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:17.363342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:20.309192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:23.272987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:26.387251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:29.392545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:32.619230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.641689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.634993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:41.704206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:44.909134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:48.218813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:51.334898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:54.337714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.660188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:00.971674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:04.418791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:08.272742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:11.250464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:14.303715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:17.513907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:20.457104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:23.436945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:26.521816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:29.537213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:32.763410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.789651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.795973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:41.853207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:45.070812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:48.388494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:51.478493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:54.483077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.828710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:01.147222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:04.587294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:08.422073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:11.498380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:14.446852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:17.660458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:20.591432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:23.587717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:26.680069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:29.686351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:32.929107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:35.932302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:38.945585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:42.005932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:45.239156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:48.535136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:51.636856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:54.631229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:57.992242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-24T18:56:37.680126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:40.744657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:43.996929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:47.171656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:50.362997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:53.355233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:56.646997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:59.905840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:03.209836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:06.718622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:10.356224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:13.372725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:16.329476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:19.530162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:22.505675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:25.607364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:28.584638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:31.617949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:34.844194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:37.864005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:40.927886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:44.166103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:47.384769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:50.547662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:53.534515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:56:56.824468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:00.096733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-24T18:57:03.401986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-24T18:57:21.871531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Number.of.BagsAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersCategory.Two.Defectsaltitude_low_metersaltitude_high_metersaltitude_mean_metersCountry.of.OriginBag.WeightIn.Country.PartnerHarvest.YearVarietyProcessing.MethodColorCertification.BodyCertification.AddressCertification.Contactunit_of_measurement
Number.of.Bags1.0000.0090.0300.0280.0440.0710.0530.0630.0210.0370.0300.065-0.111-0.0460.1550.1560.1210.1100.1170.3220.3560.3450.2100.2640.2030.1000.3430.3420.3420.101
Aroma0.0091.0000.7150.6670.6160.5610.6160.1160.1610.0240.6450.752-0.163-0.0740.003-0.1600.2140.2290.2280.1520.0340.1300.0990.0670.0160.0500.1330.1220.1300.011
Flavor0.0300.7151.0000.8060.7440.6690.7260.1600.1990.0500.7970.863-0.202-0.0960.016-0.1680.1730.1850.1860.2040.0560.1890.1080.0280.0630.0870.1910.1830.1890.000
Aftertaste0.0280.6670.8061.0000.6970.6760.7570.1580.1770.0200.7800.840-0.214-0.084-0.004-0.1740.1850.2010.2000.2690.1400.2460.1600.0970.0740.1000.2470.2480.2460.054
Acidity0.0440.6160.7440.6971.0000.6160.6600.1050.1090.0130.6880.769-0.191-0.1030.041-0.1170.2300.2420.2430.2130.0000.1670.1080.0000.0000.0880.1690.1610.1670.034
Body0.0710.5610.6690.6760.6161.0000.6970.0340.073-0.0700.6700.726-0.235-0.0370.015-0.1050.1630.1780.1750.1700.0000.1820.0000.0680.0730.0610.1840.1760.1820.000
Balance0.0530.6160.7260.7570.6600.6971.0000.1250.144-0.0230.7560.810-0.281-0.0580.004-0.1650.2150.2390.2360.2140.0610.1860.0800.0490.1290.1020.1840.1800.1820.000
Uniformity0.0630.1160.1600.1580.1050.0340.1251.0000.6210.4680.1440.3450.015-0.1560.051-0.0840.0590.0690.0680.0450.0000.0000.0000.0000.0320.0000.0000.0000.0470.110
Clean.Cup0.0210.1610.1990.1770.1090.0730.1440.6211.0000.4870.1790.3640.027-0.1430.025-0.0800.0760.0790.0820.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.124
Sweetness0.0370.0240.0500.0200.013-0.070-0.0230.4680.4871.0000.0370.1910.114-0.1190.041-0.0050.0090.0130.0140.1570.0000.0000.0000.0000.0690.0000.0000.0000.0000.075
Cupper.Points0.0300.6450.7970.7800.6880.6700.7560.1440.1790.0371.0000.856-0.232-0.0940.015-0.1770.2010.2170.2170.2030.2440.1930.3020.0550.0690.1410.1950.1880.1940.000
Total.Cup.Points0.0650.7520.8630.8400.7690.7260.8100.3450.3640.1910.8561.000-0.206-0.1200.021-0.1710.2350.2510.2520.1890.1080.1530.0000.0000.0680.0710.1560.1510.1540.064
Moisture-0.111-0.163-0.202-0.214-0.191-0.235-0.2810.0150.0270.114-0.232-0.2061.0000.032-0.0760.162-0.065-0.084-0.0770.2430.2320.2800.4480.1450.0550.2480.2810.2770.2800.115
Category.One.Defects-0.046-0.074-0.096-0.084-0.103-0.037-0.058-0.156-0.143-0.119-0.094-0.1200.0321.0000.0030.270-0.031-0.014-0.0240.0000.0000.0000.0000.0000.0510.0860.0000.2620.2670.020
Quakers0.1550.0030.016-0.0040.0410.0150.0040.0510.0250.0410.0150.021-0.0760.0031.0000.087-0.018-0.045-0.0340.1570.0760.0860.0000.0000.1650.0000.0910.0720.0860.000
Category.Two.Defects0.156-0.160-0.168-0.174-0.117-0.105-0.165-0.084-0.080-0.005-0.177-0.1710.1620.2700.0871.0000.0530.0260.0370.0000.0000.0470.0000.0920.0000.1330.0550.2350.2400.000
altitude_low_meters0.1210.2140.1730.1850.2300.1630.2150.0590.0760.0090.2010.235-0.065-0.031-0.0180.0531.0000.9390.9820.0380.0000.2340.0000.0000.0000.0000.2360.2280.2340.000
altitude_high_meters0.1100.2290.1850.2010.2420.1780.2390.0690.0790.0130.2170.251-0.084-0.014-0.0450.0260.9391.0000.9840.0380.0000.2340.0000.0000.0000.0000.2360.2280.2340.000
altitude_mean_meters0.1170.2280.1860.2000.2430.1750.2360.0680.0820.0140.2170.252-0.077-0.024-0.0340.0370.9820.9841.0000.0380.0000.2340.0000.0000.0000.0000.2360.2280.2340.000
Country.of.Origin0.3220.1520.2040.2690.2130.1700.2140.0450.0210.1570.2030.1890.2430.0000.1570.0000.0380.0380.0381.0000.3710.6540.1760.4770.3970.2780.6680.6200.6540.799
Bag.Weight0.3560.0340.0560.1400.0000.0000.0610.0000.0000.0000.2440.1080.2320.0000.0760.0000.0000.0000.0000.3711.0000.4270.3440.3390.3440.2360.4370.4030.4260.674
In.Country.Partner0.3450.1300.1890.2460.1670.1820.1860.0000.0000.0000.1930.1530.2800.0000.0860.0470.2340.2340.2340.6540.4271.0000.2840.4200.3540.3041.0000.9990.9800.576
Harvest.Year0.2100.0990.1080.1600.1080.0000.0800.0000.0000.0000.3020.0000.4480.0000.0000.0000.0000.0000.0000.1760.3440.2841.0000.2250.2490.1670.2920.2630.2840.159
Variety0.2640.0670.0280.0970.0000.0680.0490.0000.0000.0000.0550.0000.1450.0000.0000.0920.0000.0000.0000.4770.3390.4200.2251.0000.2840.2020.4290.3970.4200.604
Processing.Method0.2030.0160.0630.0740.0000.0730.1290.0320.0000.0690.0690.0680.0550.0510.1650.0000.0000.0000.0000.3970.3440.3540.2490.2841.0000.0920.3560.3520.3560.102
Color0.1000.0500.0870.1000.0880.0610.1020.0000.0000.0000.1410.0710.2480.0860.0000.1330.0000.0000.0000.2780.2360.3040.1670.2020.0921.0000.3010.3030.3000.091
Certification.Body0.3430.1330.1910.2470.1690.1840.1840.0000.0000.0000.1950.1560.2810.0000.0910.0550.2360.2360.2360.6680.4371.0000.2920.4290.3560.3011.0000.9981.0000.576
Certification.Address0.3420.1220.1830.2480.1610.1760.1800.0000.0000.0000.1880.1510.2770.2620.0720.2350.2280.2280.2280.6200.4030.9990.2630.3970.3520.3030.9981.0000.9990.575
Certification.Contact0.3420.1300.1890.2460.1670.1820.1820.0470.0000.0000.1940.1540.2800.2670.0860.2400.2340.2340.2340.6540.4260.9800.2840.4200.3560.3001.0000.9991.0000.576
unit_of_measurement0.1010.0110.0000.0540.0340.0000.0000.1100.1240.0750.0000.0640.1150.0200.0000.0000.0000.0000.0000.7990.6740.5760.1590.6040.1020.0910.5760.5750.5761.000

Missing values

2023-07-24T18:57:07.106464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-24T18:57:08.079886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-24T18:57:08.615498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SpeciesOwnerCountry.of.OriginFarm.NameLot.NumberMillICO.NumberCompanyAltitudeRegionProducerNumber.of.BagsBag.WeightIn.Country.PartnerHarvest.YearGrading.DateOwner.1VarietyProcessing.MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersColorCategory.Two.DefectsExpirationCertification.BodyCertification.AddressCertification.Contactunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_meters
1Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcNaNWashed / Wet8.678.838.678.758.508.4210.0010.010.008.7590.580.1200.0Green0April 3rd, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1950.02200.02075.0
2Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcOtherWashed / Wet8.758.678.508.588.428.4210.0010.010.008.5889.920.1200.0Green1April 3rd, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1950.02200.02075.0
3Arabicagrounds for health adminGuatemalasan marcos barrancas "san cristobal cuchNaNNaNNaNNaN1600 - 1800 mNaNNaN51Specialty Coffee AssociationNaNMay 31st, 2010Grounds for Health AdminBourbonNaN8.428.508.428.428.338.4210.0010.010.009.2589.750.0000.0NaN0May 31st, 2011Specialty Coffee Association36d0d00a3724338ba7937c52a378d085f2172daa0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660m1600.01800.01700.0
4Arabicayidnekachew dabessaEthiopiayidnekachew dabessa coffee plantationNaNwolensuNaNyidnekachew debessa coffee plantation1800-2200oromiaYidnekachew Dabessa Coffee Plantation32060 kgMETAD Agricultural Development plc2014March 26th, 2015Yidnekachew DabessaNaNNatural / Dry8.178.588.428.428.508.2510.0010.010.008.6789.000.1100.0Green2March 25th, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1800.02200.02000.0
5Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcOtherWashed / Wet8.258.508.258.508.428.3310.0010.010.008.5888.830.1200.0Green2April 3rd, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1950.02200.02075.0
6Arabicaji-ae ahnBrazilNaNNaNNaNNaNNaNNaNNaNNaN10030 kgSpecialty Coffee Institute of Asia2013September 3rd, 2013Ji-Ae AhnNaNNatural / Dry8.588.428.428.508.258.3310.0010.010.008.3388.830.1100.0Bluish-Green1September 3rd, 2014Specialty Coffee Institute of Asia726e4891cf2c9a4848768bd34b668124d12c4224b70da261fcc84831e3e9620c30a8701540abc200mNaNNaNNaN
7Arabicahugo valdiviaPeruNaNNaNhvcNaNrichmond investment-coffee departmentNaNNaNHVC10069 kgSpecialty Coffee Institute of Asia2012September 17th, 2012Hugo ValdiviaOtherWashed / Wet8.428.508.338.508.258.2510.0010.010.008.5088.750.1100.0Bluish-Green0September 17th, 2013Specialty Coffee Institute of Asia726e4891cf2c9a4848768bd34b668124d12c4224b70da261fcc84831e3e9620c30a8701540abc200mNaNNaNNaN
8Arabicaethiopia commodity exchangeEthiopiaaolmeNaNc.p.w.e010/0338NaN1570-1700oromiaBazen Agricultural & Industrial Dev't Plc30060 kgEthiopia Commodity ExchangeMarch 2010September 2nd, 2010Ethiopia Commodity ExchangeNaNNaN8.258.338.508.428.338.5010.0010.09.339.0088.670.0300.0NaN0September 2nd, 2011Ethiopia Commodity Exchangea176532400aebdc345cf3d870f84ed3ecab6249e61bbaf6a9f341e5782b8e7bd3ebf76aac89fe24bm1570.01700.01635.0
9Arabicaethiopia commodity exchangeEthiopiaaolmeNaNc.p.w.e010/0338NaN1570-1700oromiyaBazen Agricultural & Industrial Dev't Plc30060 kgEthiopia Commodity ExchangeMarch 2010September 2nd, 2010Ethiopia Commodity ExchangeNaNNaN8.678.678.588.428.338.429.3310.09.338.6788.420.0300.0NaN0September 2nd, 2011Ethiopia Commodity Exchangea176532400aebdc345cf3d870f84ed3ecab6249e61bbaf6a9f341e5782b8e7bd3ebf76aac89fe24bm1570.01700.01635.0
10Arabicadiamond enterprise plcEthiopiatulla coffee farmNaNtulla coffee farm2014/15diamond enterprise plc1795-1850snnp/kaffa zone,gimboweredaDiamond Enterprise Plc5060 kgMETAD Agricultural Development plc2014March 30th, 2015Diamond Enterprise PlcOtherNatural / Dry8.088.588.508.507.678.4210.0010.010.008.5088.250.1000.0Green4March 29th, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1795.01850.01822.5
SpeciesOwnerCountry.of.OriginFarm.NameLot.NumberMillICO.NumberCompanyAltitudeRegionProducerNumber.of.BagsBag.WeightIn.Country.PartnerHarvest.YearGrading.DateOwner.1VarietyProcessing.MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersColorCategory.Two.DefectsExpirationCertification.BodyCertification.AddressCertification.Contactunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_meters
1302Arabicakurt kappeliMexicovariousNaNf.i.e.c.h.0016-2847-0001globus coffee1000 meterssierra norte yajalon, chiapasvarious small producers2802 kgSpecialty Coffee Association2014May 5th, 2014Kurt KappeliTypicaWashed / Wet6.927.006.836.927.426.926.006.0010.006.7570.750.1200.0Green1May 5th, 2015Specialty Coffee Association36d0d00a3724338ba7937c52a378d085f2172daa0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660m1000.001000.001000.00
1303Arabicavolcafe ltda. - brasilBrazilNaN2017/2018 - Lot 2copagNaNvolcafe ltda.NaNcerradoNaN30559 kgBrazil Specialty Coffee Association2017 / 2018October 27th, 2017Volcafe Ltda. - BrasilNaNNatural / Dry7.007.006.837.007.336.836.006.0010.006.6770.670.1101.0Green55October 27th, 2018Brazil Specialty Coffee Association3297cfa4c538e3dd03f72cc4082c54f7999e1f9d8900f0bf1d0b2bafe6807a73562c7677d57eb980mNaNNaNNaN
1304ArabicacadexsaHondurascerro buenoNaNcadexsa13-63-174cadexsa1450 msnmmarcalaOmar Acosta2751 kgInstituto Hondureño del Café2014May 15th, 2014CADEXSACatuaiWashed / Wet6.676.506.176.676.836.178.008.008.006.3369.330.1000.0Green4May 15th, 2015Instituto Hondureño del Caféb4660a57e9f8cc613ae5b8f02bfce8634c763ab47f521ca403540f81ec99daec7da19c2788393880m1450.001450.001450.00
1305ArabicacadexsaHondurascerro buenoNaNcadexsa13-63-174cadexsa1450 msnmmarcalaOmar Acosta2751 kgInstituto Hondureño del Café2014May 15th, 2014CADEXSACatuaiWashed / Wet7.006.176.176.676.506.178.008.008.006.5069.170.1000.0Green3May 15th, 2015Instituto Hondureño del Caféb4660a57e9f8cc613ae5b8f02bfce8634c763ab47f521ca403540f81ec99daec7da19c2788393880m1450.001450.001450.00
1306ArabicacadexsaHondurascerro buenoNaNcadexsa13-63-174cadexsa1450 msnmmarcalaOmar Acosta2751 kgInstituto Hondureño del Café2014May 15th, 2014CADEXSACatuaiWashed / Wet7.006.336.176.506.676.178.008.008.006.3369.170.1000.0Green4May 15th, 2015Instituto Hondureño del Caféb4660a57e9f8cc613ae5b8f02bfce8634c763ab47f521ca403540f81ec99daec7da19c2788393880m1450.001450.001450.00
1307Arabicajuan carlos garcia lopezMexicoel centenarioNaNla esperanza, municipio juchique de ferrer, veracruz1104328663terra mia900juchique de ferrerJUAN CARLOS GARCÍA LOPEZ121 kgAMECAFE2012September 17th, 2012JUAN CARLOS GARCIA LOPEZBourbonWashed / Wet7.086.836.257.427.256.7510.000.0010.006.7568.330.1100.0None20September 17th, 2013AMECAFE59e396ad6e22a1c22b248f958e1da2bd8af852720eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7m900.00900.00900.00
1308Arabicamyriam kaplan-pasternakHaiti200 farmsNaNcoeb koperativ ekselsyo basen (350 members)NaNhaiti coffee~350mdepartment d'artibonite , haitiCOEB Koperativ Ekselsyo Basen12 kgSpecialty Coffee Association2012May 24th, 2012Myriam Kaplan-PasternakTypicaNatural / Dry6.756.586.426.677.086.679.336.006.006.4267.920.1480.0Blue-Green16May 24th, 2013Specialty Coffee Association36d0d00a3724338ba7937c52a378d085f2172daa0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660m350.00350.00350.00
1309Arabicaexportadora atlantic, s.a.Nicaraguafinca las marías017-053-0211/ 017-053-0212beneficio atlantic condega017-053-0211/ 017-053-0212exportadora atlantic s.a1100jalapaTeófilo Narváez55069 kgInstituto Hondureño del Café2016June 6th, 2017Exportadora Atlantic, S.A.CaturraOther7.256.586.336.256.426.086.006.006.006.1763.080.1310.0Green5June 6th, 2018Instituto Hondureño del Caféb4660a57e9f8cc613ae5b8f02bfce8634c763ab47f521ca403540f81ec99daec7da19c2788393880m1100.001100.001100.00
1310Arabicajuan luis alvarado romeroGuatemalafinca el limonNaNbeneficio serben11/853/165unicafe4650nuevo orienteWILLIAM ESTUARDO MARTINEZ PACHECO2751 kgAsociacion Nacional Del Café2012May 24th, 2012Juan Luis Alvarado RomeroCatuaiWashed / Wet7.506.676.677.677.336.678.001.331.336.6759.830.1000.0Green4May 24th, 2013Asociacion Nacional Del Caféb1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53724f04ad10ed31dbb9d260f0dfd221ba48be8a95ft1417.321417.321417.32
1312Arabicabismarck castroHonduraslos hicaques103cigrah s.a de c.v.13-111-053cigrah s.a de c.v1400comayaguaReinerio Zepeda27569 kgInstituto Hondureño del Café2017April 28th, 2017Bismarck CastroCaturraNaN0.000.000.000.000.000.000.000.000.000.000.000.1200.0Green2April 28th, 2018Instituto Hondureño del Caféb4660a57e9f8cc613ae5b8f02bfce8634c763ab47f521ca403540f81ec99daec7da19c2788393880m1400.001400.001400.00